
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sweetviz as sv
from category_encoders.hashing import HashingEncoder


data = pd.read_csv('E:/IUST/Areze/DataCoSupplyChainDataset.csv',encoding='ISO-8859-1')


data.head()


data.tail()


data.sample(5)


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 180519 entries, 0 to 180518
Data columns (total 53 columns):
 #   Column                         Non-Null Count   Dtype  
---  ------                         --------------   -----  
 0   Type                           180519 non-null  object 
 1   Days for shipping (real)       180519 non-null  int64  
 2   Days for shipment (scheduled)  180519 non-null  int64  
 3   Benefit per order              180519 non-null  float64
 4   Sales per customer             180519 non-null  float64
 5   Delivery Status                180519 non-null  object 
 6   Late_delivery_risk             180519 non-null  int64  
 7   Category Id                    180519 non-null  int64  
 8   Category Name                  180519 non-null  object 
 9   Customer City                  180519 non-null  object 
 10  Customer Country               180519 non-null  object 
 11  Customer Email                 180519 non-null  object 
 12  Customer Fname                 180519 non-null  object 
 13  Customer Id                    180519 non-null  int64  
 14  Customer Lname                 180511 non-null  object 
 15  Customer Password              180519 non-null  object 
 16  Customer Segment               180519 non-null  object 
 17  Customer State                 180519 non-null  object 
 18  Customer Street                180519 non-null  object 
 19  Customer Zipcode               180516 non-null  float64
 20  Department Id                  180519 non-null  int64  
 21  Department Name                180519 non-null  object 
 22  Latitude                       180519 non-null  float64
 23  Longitude                      180519 non-null  float64
 24  Market                         180519 non-null  object 
 25  Order City                     180519 non-null  object 
 26  Order Country                  180519 non-null  object 
 27  Order Customer Id              180519 non-null  int64  
 28  order date (DateOrders)        180519 non-null  object 
 29  Order Id                       180519 non-null  int64  
 30  Order Item Cardprod Id         180519 non-null  int64  
 31  Order Item Discount            180519 non-null  float64
 32  Order Item Discount Rate       180519 non-null  float64
 33  Order Item Id                  180519 non-null  int64  
 34  Order Item Product Price       180519 non-null  float64
 35  Order Item Profit Ratio        180519 non-null  float64
 36  Order Item Quantity            180519 non-null  int64  
 37  Sales                          180519 non-null  float64
 38  Order Item Total               180519 non-null  float64
 39  Order Profit Per Order         180519 non-null  float64
 40  Order Region                   180519 non-null  object 
 41  Order State                    180519 non-null  object 
 42  Order Status                   180519 non-null  object 
 43  Order Zipcode                  24840 non-null   float64
 44  Product Card Id                180519 non-null  int64  
 45  Product Category Id            180519 non-null  int64  
 46  Product Description            0 non-null       float64
 47  Product Image                  180519 non-null  object 
 48  Product Name                   180519 non-null  object 
 49  Product Price                  180519 non-null  float64
 50  Product Status                 180519 non-null  int64  
 51  shipping date (DateOrders)     180519 non-null  object 
 52  Shipping Mode                  180519 non-null  object 
dtypes: float64(15), int64(14), object(24)
memory usage: 73.0+ MB


data.describe()


data.columns

Index(['Type', 'Days for shipping (real)', 'Days for shipment (scheduled)',
       'Benefit per order', 'Sales per customer', 'Delivery Status',
       'Late_delivery_risk', 'Category Id', 'Category Name', 'Customer City',
       'Customer Country', 'Customer Email', 'Customer Fname', 'Customer Id',
       'Customer Lname', 'Customer Password', 'Customer Segment',
       'Customer State', 'Customer Street', 'Customer Zipcode',
       'Department Id', 'Department Name', 'Latitude', 'Longitude', 'Market',
       'Order City', 'Order Country', 'Order Customer Id',
       'order date (DateOrders)', 'Order Id', 'Order Item Cardprod Id',
       'Order Item Discount', 'Order Item Discount Rate', 'Order Item Id',
       'Order Item Product Price', 'Order Item Profit Ratio',
       'Order Item Quantity', 'Sales', 'Order Item Total',
       'Order Profit Per Order', 'Order Region', 'Order State', 'Order Status',
       'Order Zipcode', 'Product Card Id', 'Product Category Id',
       'Product Description', 'Product Image', 'Product Name', 'Product Price',
       'Product Status', 'shipping date (DateOrders)', 'Shipping Mode'],
      dtype='object')


data['Type'].value_counts()

DEBIT       69295
TRANSFER    49883
PAYMENT     41725
CASH        19616
Name: Type, dtype: int64


data['Days for shipping (real)'].value_counts()

2    56618
3    28765
6    28723
4    28513
5    28163
0     5080
1     4657
Name: Days for shipping (real), dtype: int64


data['Days for shipment (scheduled)'].value_counts()

4    107752
2     35216
1     27814
0      9737
Name: Days for shipment (scheduled), dtype: int64


data['Benefit per order'].value_counts()

 0.000000      1177
 143.990005     199
 72.000000      194
 46.799999      188
 24.000000      181
               ... 
-48.830002        1
 48.220001        1
-145.729996       1
-330.109985       1
-337.100006       1
Name: Benefit per order, Length: 21998, dtype: int64


data['Sales per customer'].value_counts()

122.839996    1264
109.190002    1247
124.790001    1243
129.990005    1243
116.989998    1243
              ... 
455.950012       1
470.250000       1
424.790008       1
98.010002        1
474.760010       1
Name: Sales per customer, Length: 2927, dtype: int64


data['Delivery Status'].value_counts()

Late delivery        98977
Advance shipping     41592
Shipping on time     32196
Shipping canceled     7754
Name: Delivery Status, dtype: int64


data['Late_delivery_risk'].value_counts()

1    98977
0    81542
Name: Late_delivery_risk, dtype: int64


data['Category Id'].value_counts()

17    24551
18    22246
24    21035
46    19298
45    17325
48    15540
43    13729
9     12487
29    10984
37     2029
40     1780
36     1475
26     1201
13     1127
35     1070
41      974
75      838
63      652
76      650
3       632
7       614
62      592
74      529
33      524
72      492
66      484
68      484
67      483
64      442
32      441
44      440
71      434
65      431
12      423
59      405
38      384
69      362
73      357
5       343
6       328
11      309
30      283
61      271
70      208
60      207
31      181
2       138
10      111
16       68
4        67
34       61
Name: Category Id, dtype: int64


data['Category Name'].value_counts()

Cleats                  24551
Men's Footwear          22246
Women's Apparel         21035
Indoor/Outdoor Games    19298
Fishing                 17325
Water Sports            15540
Camping & Hiking        13729
Cardio Equipment        12487
Shop By Sport           10984
Electronics              3156
Accessories              1780
Golf Balls               1475
Girls' Apparel           1201
Golf Gloves              1070
Trade-In                  974
Video Games               838
Children's Clothing       652
Women's Clothing          650
Baseball & Softball       632
Hockey                    614
Cameras                   592
Toys                      529
Golf Shoes                524
Pet Supplies              492
Garden                    484
Crafts                    484
DVDs                      483
Computers                 442
Golf Apparel              441
Hunting & Shooting        440
Music                     434
Consumer Electronics      431
Boxing & MMA              423
Books                     405
Kids' Golf Clubs          384
Health and Beauty         362
Sporting Goods            357
Lacrosse                  343
Tennis & Racquet          328
Fitness Accessories       309
Men's Golf Clubs          283
CDs                       271
Men's Clothing            208
Baby                      207
Women's Golf Clubs        181
Soccer                    138
Strength Training         111
As Seen on  TV!            68
Basketball                 67
Golf Bags & Carts          61
Name: Category Name, dtype: int64


data['Customer City'].value_counts()

Caguas         66770
Chicago         3885
Los Angeles     3417
Brooklyn        3412
New York        1816
               ...  
Bartlett          25
Malden            22
Ponce             22
Freehold          13
CA                 3
Name: Customer City, Length: 563, dtype: int64


data['Customer Country'].value_counts()

EE. UU.        111146
Puerto Rico     69373
Name: Customer Country, dtype: int64


CustomerCountry_map = {'EE. UU.':'USA','Puerto Rico':'Puerto Rico'}
data['Customer Country'] = data['Customer Country'].map(CustomerCountry_map)


data['Customer Country'].value_counts()

USA            111146
Puerto Rico     69373
Name: Customer Country, dtype: int64


data['Customer Email'].value_counts()

XXXXXXXXX    180519
Name: Customer Email, dtype: int64


data['Customer Fname'].value_counts()

Mary       65150
James       1835
Robert      1759
Michael     1680
David       1625
           ...  
Lina           1
Cami           1
Ana            1
Nadin          1
Brees          1
Name: Customer Fname, Length: 782, dtype: int64


data['Customer Id'].value_counts()

5654     47
10591    45
5004     45
5715     44
3708     44
         ..
18698     1
15537     1
15484     1
20681     1
2677      1
Name: Customer Id, Length: 20652, dtype: int64


data[data['Customer Id']==5654]


data['Customer Lname'].value_counts()

Smith       64104
Johnson       989
Brown         909
Williams      869
Jones         859
            ...  
Dias            1
Will            1
Godinho         1
Mcke            1
Harri           1
Name: Customer Lname, Length: 1109, dtype: int64


data['Customer Password'].value_counts()

XXXXXXXXX    180519
Name: Customer Password, dtype: int64


data['Customer Segment'].value_counts()

Consumer       93504
Corporate      54789
Home Office    32226
Name: Customer Segment, dtype: int64


data['Customer State'].value_counts()

PR       69373
CA       29223
NY       11327
TX        9103
IL        7631
FL        5456
OH        4095
PA        3824
MI        3804
NJ        3191
AZ        3026
GA        2503
MD        2415
NC        1992
CO        1914
VA        1849
OR        1668
MA        1607
TN        1582
NV        1440
MO        1354
HI        1248
CT        1094
UT         968
NM         949
LA         948
WA         920
WI         850
MN         672
SC         665
IN         581
DC         579
KY         487
KS         458
DE         269
RI         243
WV         241
OK         232
ND         215
ID         167
AR         164
MT          87
IA          67
AL          35
95758        2
91732        1
Name: Customer State, dtype: int64


data[data['Customer State']=='95758']


data.drop([35704,46440],inplace=True)


data[data['Customer State']=='95758']


data[data['Customer State']=='91732']


data.drop([82511],inplace=True)


data[data['Customer State']=='91732']


data['Customer Street'].value_counts()

9126 Wishing Expressway     122
4388 Burning Goose Ridge    117
4720 Noble Hills Wynd       116
2878 Hazy Wagon  Thicket    113
398 Emerald Grove           109
                           ... 
9468 Red Corner               1
7676 Colonial Towers          1
3440 Emerald Knoll            1
6425 Thunder Impasse          1
8621 Broad Forest             1
Name: Customer Street, Length: 7456, dtype: int64


data['Customer Zipcode'].value_counts()

725.0      66770
921.0        337
23455.0      334
957.0        297
79109.0      292
           ...  
60636.0       18
89015.0       16
32210.0       15
7728.0        13
11225.0        9
Name: Customer Zipcode, Length: 995, dtype: int64


data['Department Id'].value_counts()

7     66861
4     48997
5     33220
3     14525
6      9686
2      2479
9      2025
10     1464
11      492
8       405
12      362
Name: Department Id, dtype: int64


data['Department Name'].value_counts()

Fan Shop              66861
Apparel               48997
Golf                  33220
Footwear              14525
Outdoors               9686
Fitness                2479
Discs Shop             2025
Technology             1464
Pet Shop                492
Book Shop               405
Health and Beauty       362
Name: Department Name, dtype: int64


data['Latitude'].value_counts()

18.227573    417
39.495914    370
18.227577    300
36.910831    280
26.098499    270
            ... 
18.246769      1
18.214268      1
18.253412      1
18.287685      1
18.242485      1
Name: Latitude, Length: 11248, dtype: int64


data['Longitude'].value_counts()

-66.370613     3821
-66.370575     3523
-66.370590     3522
-66.370506     3465
-66.370552     3417
               ... 
-77.381859        1
-118.428261       1
-119.781486       1
-88.943802        1
-112.236633       1
Name: Longitude, Length: 4485, dtype: int64


data['Market'].value_counts()

LATAM           51594
Europe          50250
Pacific Asia    41259
USCA            25799
Africa          11614
Name: Market, dtype: int64


data['Order City'].value_counts()

Santo Domingo    2211
New York City    2202
Los Angeles      1845
Tegucigalpa      1783
Managua          1682
                 ... 
Libourne            1
Bergerac            1
Juliaca             1
Takasaki            1
Pernik              1
Name: Order City, Length: 3597, dtype: int64


data['Order Country'].value_counts()

Estados Unidos       24840
Francia              13221
México               13172
Alemania              9564
Australia             8497
                     ...  
Guinea Ecuatorial        2
Kuwait                   2
Sáhara Occidental        2
Burundi                  1
Serbia                   1
Name: Order Country, Length: 164, dtype: int64


data['Order Customer Id'].value_counts()

5654     47
10591    45
5004     45
3708     44
5715     44
         ..
15620     1
18698     1
15537     1
15484     1
2677      1
Name: Order Customer Id, Length: 20649, dtype: int64


data['order date (DateOrders)'].value_counts()

12/14/2016 12:29    5
9/22/2015 3:50      5
12/31/2016 9:50     5
9/23/2015 18:22     5
2/11/2016 16:35     5
                   ..
9/5/2016 5:58       1
9/6/2016 1:56       1
9/6/2016 3:20       1
9/6/2016 19:27      1
1/17/2016 5:56      1
Name: order date (DateOrders), Length: 65749, dtype: int64


data['order date (DateOrders)'] = data['order date (DateOrders)'].apply(lambda x:pd.to_datetime(x))


data['DateOrders'] = data['order date (DateOrders)'].apply(lambda x:x.date())


data['TimeOrders'] = data['order date (DateOrders)'].apply(lambda x:x.time())


data['DayOrders'] = data['order date (DateOrders)'].apply(lambda x:x.day)


data['MonthOrders'] = data['order date (DateOrders)'].apply(lambda x:x.month)


data['YearOrders'] = data['order date (DateOrders)'].apply(lambda x:x.year)


data['DayOfWeekOrders'] = data['order date (DateOrders)'].apply(lambda x:x.dayofweek)


dayofweek_map = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}
data['DayOfWeekOrders'] = data['DayOfWeekOrders'].map(dayofweek_map)


data['HourOrders'] = data['order date (DateOrders)'].apply(lambda x:x.hour)


data['MinutesOrders'] = data['order date (DateOrders)'].apply(lambda x:x.minute)


data.head(5)


data['Order Id'].value_counts()

48880    5
18097    5
50037    5
18207    5
27861    5
        ..
42011    1
42068    1
42072    1
42118    1
26118    1
Name: Order Id, Length: 65749, dtype: int64


data['Order Item Cardprod Id'].value_counts()

365     24515
403     22246
502     21035
1014    19298
1004    17325
        ...  
127        27
208        15
860        11
226        10
60         10
Name: Order Item Cardprod Id, Length: 118, dtype: int64


data['Order Item Discount'].value_counts()

0.000000      10028
6.000000       4589
12.000000      4067
4.000000       3647
8.000000       3626
              ...  
23.750000         1
96.000000         1
400.000000        1
31.980000         1
61.189999         1
Name: Order Item Discount, Length: 1017, dtype: int64


data['Order Item Discount Rate'].value_counts()

0.04    10029
0.13    10029
0.20    10029
0.18    10029
0.17    10029
0.05    10029
0.15    10029
0.03    10029
0.12    10029
0.10    10029
0.09    10029
0.07    10029
0.06    10029
0.25    10028
0.00    10028
0.01    10028
0.16    10028
0.02    10027
Name: Order Item Discount Rate, dtype: int64


data['Order Item Id'].value_counts()

180517    1
163540    1
39851     1
173601    1
46791     1
         ..
14991     1
150102    1
138584    1
150270    1
65113     1
Name: Order Item Id, Length: 180516, dtype: int64


data['Order Item Product Price'].value_counts()

59.990002      24820
129.990005     22372
50.000000      21035
49.980000      19298
399.980011     17325
               ...  
349.989990        40
329.989990        27
599.989990        21
1999.989990       15
999.989990        10
Name: Order Item Product Price, Length: 75, dtype: int64


data['Order Item Profit Ratio'].value_counts()

 0.48    9197
 0.35    7996
 0.26    6577
 0.34    6507
 0.47    6378
         ... 
-2.07      18
-0.90      18
-2.30      18
-1.10      18
-2.10      18
Name: Order Item Profit Ratio, Length: 162, dtype: int64


data['Order Item Quantity'].value_counts()

1    99131
5    20385
3    20350
4    20335
2    20315
Name: Order Item Quantity, dtype: int64


data['Sales'].value_counts()

129.990005    22372
399.980011    17325
199.990005    15622
299.980011    13729
179.970001     5016
              ...  
999.989990       10
379.959992       10
198.000000        9
396.000000        9
99.000000         9
Name: Sales, Length: 193, dtype: int64


data['Order Item Total'].value_counts()

122.839996    1264
109.190002    1247
124.790001    1243
129.990005    1243
116.989998    1243
              ... 
455.950012       1
470.250000       1
424.790008       1
98.010002        1
474.760010       1
Name: Order Item Total, Length: 2927, dtype: int64


data['Order Profit Per Order'].value_counts()

 0.000000      1177
 143.990005     199
 72.000000      194
 46.799999      188
 24.000000      181
               ... 
-48.830002        1
 48.220001        1
-145.729996       1
-330.109985       1
-337.100006       1
Name: Order Profit Per Order, Length: 21998, dtype: int64


data['Order Region'].value_counts()

Central America    28341
Western Europe     27108
South America      14935
Oceania            10148
Northern Europe     9791
Southeast Asia      9538
Southern Europe     9431
Caribbean           8318
West of USA         7993
South Asia          7731
Eastern Asia        7280
East of USA         6915
West Asia           6009
US Center           5887
South of  USA       4045
Eastern Europe      3920
West Africa         3696
North Africa        3232
East Africa         1852
Central Africa      1677
Southern Africa     1157
Canada               959
Central Asia         553
Name: Order Region, dtype: int64


data['Order State'].value_counts()

Inglaterra                     6721
California                     4966
Isla de Francia                4580
Renania del Norte-Westfalia    3303
San Salvador                   3055
                               ... 
Kabarole                          1
Vrancea                           1
Buyumbura Mairie                  1
Aomori                            1
Iringa                            1
Name: Order State, Length: 1089, dtype: int64


data['Order Status'].value_counts()

COMPLETE           59489
PENDING_PAYMENT    39831
PROCESSING         21902
PENDING            20227
CLOSED             19616
ON_HOLD             9804
SUSPECTED_FRAUD     4062
CANCELED            3692
PAYMENT_REVIEW      1893
Name: Order Status, dtype: int64


data['Order Zipcode'].value_counts()

10035.0    648
10009.0    550
10024.0    541
94122.0    526
10011.0    463
          ... 
63376.0      1
31088.0      1
32174.0      1
60477.0      1
61832.0      1
Name: Order Zipcode, Length: 609, dtype: int64


data['Product Card Id'].value_counts()

365     24515
403     22246
502     21035
1014    19298
1004    17325
        ...  
127        27
208        15
860        11
226        10
60         10
Name: Product Card Id, Length: 118, dtype: int64


data['Product Category Id'].value_counts()

17    24551
18    22246
24    21035
46    19298
45    17325
48    15540
43    13729
9     12487
29    10984
37     2029
40     1780
36     1475
26     1201
13     1127
35     1070
41      974
75      837
63      651
76      650
3       632
7       614
62      592
74      529
33      524
72      492
66      484
68      484
67      483
64      442
32      441
44      440
71      434
65      430
12      423
59      405
38      384
69      362
73      357
5       343
6       328
11      309
30      283
61      271
70      208
60      207
31      181
2       138
10      111
16       68
4        67
34       61
Name: Product Category Id, dtype: int64


data['Product Description'].value_counts()

Series([], Name: Product Description, dtype: int64)


data['Product Image'].value_counts()

http://images.acmesports.sports/Perfect+Fitness+Perfect+Rip+Deck                                 24515
http://images.acmesports.sports/Nike+Men%27s+CJ+Elite+2+TD+Football+Cleat                        22246
http://images.acmesports.sports/Nike+Men%27s+Dri-FIT+Victory+Golf+Polo                           21035
http://images.acmesports.sports/O%27Brien+Men%27s+Neoprene+Life+Vest                             19298
http://images.acmesports.sports/Field+%26+Stream+Sportsman+16+Gun+Fire+Safe                      17325
                                                                                                 ...  
http://images.acmesports.sports/Stiga+Master+Series+ST3100+Competition+Indoor+Table+Tennis...       27
http://images.acmesports.sports/SOLE+E35+Elliptical                                                 15
http://images.acmesports.sports/Bushnell+Pro+X7+Jolt+Slope+Rangefinder                              11
http://images.acmesports.sports/Bowflex+SelectTech+1090+Dumbbells                                   10
http://images.acmesports.sports/SOLE+E25+Elliptical                                                 10
Name: Product Image, Length: 118, dtype: int64


data['Product Name'].value_counts()

Perfect Fitness Perfect Rip Deck                 24515
Nike Men's CJ Elite 2 TD Football Cleat          22246
Nike Men's Dri-FIT Victory Golf Polo             21035
O'Brien Men's Neoprene Life Vest                 19298
Field & Stream Sportsman 16 Gun Fire Safe        17325
                                                 ...  
Stiga Master Series ST3100 Competition Indoor       27
SOLE E35 Elliptical                                 15
Bushnell Pro X7 Jolt Slope Rangefinder              11
Bowflex SelectTech 1090 Dumbbells                   10
SOLE E25 Elliptical                                 10
Name: Product Name, Length: 118, dtype: int64


data['Product Price'].value_counts()

59.990002      24820
129.990005     22372
50.000000      21035
49.980000      19298
399.980011     17325
               ...  
349.989990        40
329.989990        27
599.989990        21
1999.989990       15
999.989990        10
Name: Product Price, Length: 75, dtype: int64


data['Product Status'].value_counts()

0    180516
Name: Product Status, dtype: int64


data['shipping date (DateOrders)'].value_counts()

1/4/2017 17:11     10
5/21/2015 10:19    10
7/5/2017 10:59     10
12/9/2015 9:41     10
5/21/2015 15:34    10
                   ..
4/2/2015 12:40      1
4/2/2015 23:10      1
3/31/2015 7:03      1
4/3/2015 22:07      1
1/21/2016 5:56      1
Name: shipping date (DateOrders), Length: 63699, dtype: int64


data['shipping date (DateOrders)'] = data['shipping date (DateOrders)'].apply(lambda x:pd.to_datetime(x))


data['shipping Date'] = data['shipping date (DateOrders)'].apply(lambda x:x.date())


data['shipping Time'] = data['shipping date (DateOrders)'].apply(lambda x:x.time())


data['shipping Day'] = data['shipping date (DateOrders)'].apply(lambda x:x.day)


data['shipping Month'] = data['shipping date (DateOrders)'].apply(lambda x:x.month)


data['shipping Year'] = data['shipping date (DateOrders)'].apply(lambda x:x.year)


data['DayOfWeek Shipping'] = data['shipping date (DateOrders)'].apply(lambda x:x.dayofweek)


dayofweek_map = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}
data['DayOfWeek Shipping'] = data['DayOfWeek Shipping'].map(dayofweek_map)


data['shipping Hour'] = data['shipping date (DateOrders)'].apply(lambda x:x.hour)


data['shipping Minute'] = data['shipping date (DateOrders)'].apply(lambda x:x.minute)


data.head(2)


data['Shipping Mode'].value_counts()

Standard Class    107750
Second Class       35216
First Class        27813
Same Day            9737
Name: Shipping Mode, dtype: int64


sns.heatmap(data=data.isnull(),cmap='Purples_r',cbar=False)

<AxesSubplot:>


data.drop(['Product Description','Order Zipcode'],axis=1,inplace=True)


data.drop(['Customer Email','Customer Fname','Customer Lname','Customer Password',
           'Customer Street','Product Image',
          'Product Status'],axis=1,inplace=True)


data


data.dropna(inplace=True)


sns.heatmap(data=data.isnull(),cmap='viridis',cbar=False)

<AxesSubplot:>


data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 180516 entries, 0 to 180518
Data columns (total 60 columns):
 #   Column                         Non-Null Count   Dtype         
---  ------                         --------------   -----         
 0   Type                           180516 non-null  object        
 1   Days for shipping (real)       180516 non-null  int64         
 2   Days for shipment (scheduled)  180516 non-null  int64         
 3   Benefit per order              180516 non-null  float64       
 4   Sales per customer             180516 non-null  float64       
 5   Delivery Status                180516 non-null  object        
 6   Late_delivery_risk             180516 non-null  int64         
 7   Category Id                    180516 non-null  int64         
 8   Category Name                  180516 non-null  object        
 9   Customer City                  180516 non-null  object        
 10  Customer Country               180516 non-null  object        
 11  Customer Id                    180516 non-null  int64         
 12  Customer Segment               180516 non-null  object        
 13  Customer State                 180516 non-null  object        
 14  Customer Zipcode               180516 non-null  float64       
 15  Department Id                  180516 non-null  int64         
 16  Department Name                180516 non-null  object        
 17  Latitude                       180516 non-null  float64       
 18  Longitude                      180516 non-null  float64       
 19  Market                         180516 non-null  object        
 20  Order City                     180516 non-null  object        
 21  Order Country                  180516 non-null  object        
 22  Order Customer Id              180516 non-null  int64         
 23  order date (DateOrders)        180516 non-null  datetime64[ns]
 24  Order Id                       180516 non-null  int64         
 25  Order Item Cardprod Id         180516 non-null  int64         
 26  Order Item Discount            180516 non-null  float64       
 27  Order Item Discount Rate       180516 non-null  float64       
 28  Order Item Id                  180516 non-null  int64         
 29  Order Item Product Price       180516 non-null  float64       
 30  Order Item Profit Ratio        180516 non-null  float64       
 31  Order Item Quantity            180516 non-null  int64         
 32  Sales                          180516 non-null  float64       
 33  Order Item Total               180516 non-null  float64       
 34  Order Profit Per Order         180516 non-null  float64       
 35  Order Region                   180516 non-null  object        
 36  Order State                    180516 non-null  object        
 37  Order Status                   180516 non-null  object        
 38  Product Card Id                180516 non-null  int64         
 39  Product Category Id            180516 non-null  int64         
 40  Product Name                   180516 non-null  object        
 41  Product Price                  180516 non-null  float64       
 42  shipping date (DateOrders)     180516 non-null  datetime64[ns]
 43  Shipping Mode                  180516 non-null  object        
 44  DateOrders                     180516 non-null  object        
 45  TimeOrders                     180516 non-null  object        
 46  DayOrders                      180516 non-null  int64         
 47  MonthOrders                    180516 non-null  int64         
 48  YearOrders                     180516 non-null  int64         
 49  DayOfWeekOrders                180516 non-null  object        
 50  HourOrders                     180516 non-null  int64         
 51  MinutesOrders                  180516 non-null  int64         
 52  shipping Date                  180516 non-null  object        
 53  shipping Time                  180516 non-null  object        
 54  shipping Day                   180516 non-null  int64         
 55  shipping Month                 180516 non-null  int64         
 56  shipping Year                  180516 non-null  int64         
 57  DayOfWeek Shipping             180516 non-null  object        
 58  shipping Hour                  180516 non-null  int64         
 59  shipping Minute                180516 non-null  int64         
dtypes: datetime64[ns](2), float64(13), int64(23), object(22)
memory usage: 84.0+ MB


data.corr()


plt.figure(figsize=(15,11))
sns.heatmap(data.corr(),cmap='viridis')

<AxesSubplot:>


#analyzing the dataset
advert_report = sv.analyze([data,'Online Shop'],target_feat='Sales',pairwise_analysis="on")
#display the report
advert_report.show_notebook(w='100%')


sns.countplot(data=data,x='Shipping Mode',hue='Late_delivery_risk',palette='Set1')

<AxesSubplot:xlabel='Shipping Mode', ylabel='count'>


data[data['Delivery Status']=='Shipping canceled']['Type'].value_counts()

TRANSFER    7754
Name: Type, dtype: int64


datasweet = data.copy()
datasweet.drop(['shipping date (DateOrders)','order date (DateOrders)'],inplace=True,axis=1)


my_report = sv.compare_intra(datasweet, datasweet['Late_delivery_risk'] ==0, ["No Risk", "Risk"])
my_report.show_notebook(w='100%')


df = data.copy()
df.drop(['Days for shipping (real)','Days for shipment (scheduled)','Benefit per order','Sales per customer',
        'Category Id','Customer Id','Customer Zipcode','Department Id','Latitude','Longitude',
        'Order Customer Id','order date (DateOrders)','Order Id','Order Item Cardprod Id','Order Item Discount',
        'Order Item Discount Rate','Order Item Id','Order Item Product Price','Order Item Profit Ratio','Order Item Quantity',
        'Sales','Order Item Total','Order Profit Per Order','Product Card Id','Product Category Id','Product Price',
        'shipping date (DateOrders)','DayOrders','MonthOrders','YearOrders','HourOrders','MinutesOrders','shipping Day',
        'shipping Month','shipping Year','shipping Hour','shipping Minute','Customer State','Department Name',
               'Order State','DateOrders','TimeOrders','shipping Date','shipping Time'],axis=1,inplace=True)


df = pd.get_dummies(df)

df


from mlxtend.frequent_patterns import apriori, association_rules


apriori = apriori(df, min_support = 0.3, use_colnames = True, verbose = 1,low_memory=True)
apriori.sort_values(by='support',ascending=False).head(30)

C:\ProgramData\Anaconda3\lib\site-packages\mlxtend\frequent_patterns\fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type
  warnings.warn(

Processing 4 combinations | Sampling itemset size 32


rules = association_rules(apriori, metric = "support", min_threshold = 0.1)
rules.head(20)


rules[rules['lift'] >= 1].sort_values(by='lift',ascending=False).head(30)


plt.scatter(rules['support'], rules['confidence'], alpha=0.5)
plt.xlabel('support')
plt.ylabel('confidence')
plt.title('Support vs Confidence')
plt.show()


plt.scatter(rules['support'], rules['lift'], alpha=0.5)
plt.xlabel('support')
plt.ylabel('lift')
plt.title('Support vs Lift')
plt.show()


fit = np.polyfit(rules['lift'], rules['confidence'], 1)
fit_fn = np.poly1d(fit)
plt.plot(rules['lift'], rules['confidence'], 'yo', rules['lift'], 
 fit_fn(rules['lift']))

[<matplotlib.lines.Line2D at 0x2188afaca90>,
 <matplotlib.lines.Line2D at 0x2188afaca60>]


data.drop(['Product Name','Category Name','Department Name','shipping date (DateOrders)',
           'order date (DateOrders)'],axis=1,inplace=True)


data = pd.get_dummies(data,columns=['Type','Delivery Status','Customer Country','Market','Shipping Mode'],drop_first=True)
data


enc = HashingEncoder(verbose=1,n_components=8,max_process=1)


data_CCity = enc.fit_transform(data['Customer City'],axis=1)


data_CCity


data_CCity.rename(columns={'col_0':'CCity_1','col_1':'CCity_2','col_2':'CCity_3',
                          'col_3':'CCity_4','col_4':'CCity_5','col_5':'CCity_6',
                           'col_6':'CCity_7','col_7':'CCity_8'
                          },inplace=True)


data_CCity


dmap = {'Consumer':1,'Corporate':2,'Home Office':3}
data['Customer Segment'] = data['Customer Segment'].map(dmap)


data_CState = enc.fit_transform(data['Customer State'],axis=1)


data_CState.rename(columns={'col_0':'CState_1','col_1':'CState_2','col_2':'CState_3',
                          'col_3':'CState_4','col_4':'CState_5','col_5':'CState_6',
                           'col_6':'CState_7','col_7':'CState_8'
                          },inplace=True)


data_CState


data_OCity = enc.fit_transform(data['Order City'],axis=1)


data_OCity.rename(columns={'col_0':'OCity_1','col_1':'OCity_2','col_2':'OCity_3',
                          'col_3':'OCity_4','col_4':'OCity_5','col_5':'OCity_6',
                           'col_6':'OCity_7','col_7':'OCity_8'
                          },inplace=True)


data_OCity


data_OCountry = enc.fit_transform(data['Order Country'],axis=1)


data_OCountry.rename(columns={'col_0':'OCountry_1','col_1':'OCountry_2','col_2':'OCountry_3',
                          'col_3':'OCountry_4','col_4':'OCountry_5','col_5':'OCountry_6',
                           'col_6':'OCountry_7','col_7':'OCountry_8'
                          },inplace=True)


data_OCountry


data_ORegion = enc.fit_transform(data['Order Region'],axis=1)


data_ORegion.rename(columns={'col_0':'ORegion_1','col_1':'ORegion_2','col_2':'ORegion_3',
                          'col_3':'ORegion_4','col_4':'ORegion_5','col_5':'ORegion_6',
                           'col_6':'ORegion_7','col_7':'ORegion_8'
                          },inplace=True)


data_ORegion


data_OState = enc.fit_transform(data['Order State'],axis=1)


data_OState.rename(columns={'col_0':'OState_1','col_1':'OState_2','col_2':'OState_3',
                          'col_3':'OState_4','col_4':'OState_5','col_5':'OState_6',
                           'col_6':'OState_7','col_7':'OState_8'
                          },inplace=True)


data_OState


enc = HashingEncoder(verbose=1,n_components=3,max_process=1)


data_OStatus = enc.fit_transform(data['Order Status'],axis=1)


data_OStatus.rename(columns={'col_0':'OStatus_1','col_1':'OStatus_2','col_2':'OStatus_3'},inplace=True)


data_OStatus


data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 180516 entries, 0 to 180518
Data columns (total 64 columns):
 #   Column                             Non-Null Count   Dtype  
---  ------                             --------------   -----  
 0   Days for shipping (real)           180516 non-null  int64  
 1   Days for shipment (scheduled)      180516 non-null  int64  
 2   Benefit per order                  180516 non-null  float64
 3   Sales per customer                 180516 non-null  float64
 4   Late_delivery_risk                 180516 non-null  int64  
 5   Category Id                        180516 non-null  int64  
 6   Customer City                      180516 non-null  object 
 7   Customer Id                        180516 non-null  int64  
 8   Customer Segment                   180516 non-null  int64  
 9   Customer State                     180516 non-null  object 
 10  Customer Zipcode                   180516 non-null  float64
 11  Department Id                      180516 non-null  int64  
 12  Latitude                           180516 non-null  float64
 13  Longitude                          180516 non-null  float64
 14  Order City                         180516 non-null  object 
 15  Order Country                      180516 non-null  object 
 16  Order Customer Id                  180516 non-null  int64  
 17  Order Id                           180516 non-null  int64  
 18  Order Item Cardprod Id             180516 non-null  int64  
 19  Order Item Discount                180516 non-null  float64
 20  Order Item Discount Rate           180516 non-null  float64
 21  Order Item Id                      180516 non-null  int64  
 22  Order Item Product Price           180516 non-null  float64
 23  Order Item Profit Ratio            180516 non-null  float64
 24  Order Item Quantity                180516 non-null  int64  
 25  Sales                              180516 non-null  float64
 26  Order Item Total                   180516 non-null  float64
 27  Order Profit Per Order             180516 non-null  float64
 28  Order Region                       180516 non-null  object 
 29  Order State                        180516 non-null  object 
 30  Order Status                       180516 non-null  object 
 31  Product Card Id                    180516 non-null  int64  
 32  Product Category Id                180516 non-null  int64  
 33  Product Price                      180516 non-null  float64
 34  DateOrders                         180516 non-null  object 
 35  TimeOrders                         180516 non-null  object 
 36  DayOrders                          180516 non-null  int64  
 37  MonthOrders                        180516 non-null  int64  
 38  YearOrders                         180516 non-null  int64  
 39  DayOfWeekOrders                    180516 non-null  object 
 40  HourOrders                         180516 non-null  int64  
 41  MinutesOrders                      180516 non-null  int64  
 42  shipping Date                      180516 non-null  object 
 43  shipping Time                      180516 non-null  object 
 44  shipping Day                       180516 non-null  int64  
 45  shipping Month                     180516 non-null  int64  
 46  shipping Year                      180516 non-null  int64  
 47  DayOfWeek Shipping                 180516 non-null  object 
 48  shipping Hour                      180516 non-null  int64  
 49  shipping Minute                    180516 non-null  int64  
 50  Type_DEBIT                         180516 non-null  uint8  
 51  Type_PAYMENT                       180516 non-null  uint8  
 52  Type_TRANSFER                      180516 non-null  uint8  
 53  Delivery Status_Late delivery      180516 non-null  uint8  
 54  Delivery Status_Shipping canceled  180516 non-null  uint8  
 55  Delivery Status_Shipping on time   180516 non-null  uint8  
 56  Customer Country_USA               180516 non-null  uint8  
 57  Market_Europe                      180516 non-null  uint8  
 58  Market_LATAM                       180516 non-null  uint8  
 59  Market_Pacific Asia                180516 non-null  uint8  
 60  Market_USCA                        180516 non-null  uint8  
 61  Shipping Mode_Same Day             180516 non-null  uint8  
 62  Shipping Mode_Second Class         180516 non-null  uint8  
 63  Shipping Mode_Standard Class       180516 non-null  uint8  
dtypes: float64(13), int64(24), object(13), uint8(14)
memory usage: 76.7+ MB


temp = data[['Days for shipping (real)','Days for shipment (scheduled)','Benefit per order','Sales per customer',
     'Late_delivery_risk','Category Id','Customer Id','Customer Zipcode','Department Id','Latitude','Longitude',
     'Order Customer Id','Order Id','Order Item Cardprod Id','Order Item Discount','Order Item Discount Rate',
     'Order Item Id','Order Item Product Price','Order Item Profit Ratio','Order Item Quantity','Order Item Total',
     'Order Profit Per Order','Product Card Id','Product Category Id','Product Price','DayOrders','MonthOrders',
     'YearOrders','HourOrders','MinutesOrders','shipping Day','shipping Month','shipping Year','shipping Hour','shipping Minute',
     'Type_DEBIT','Type_PAYMENT','Type_TRANSFER','Delivery Status_Late delivery','Delivery Status_Shipping canceled',
     'Delivery Status_Shipping on time','Customer Country_USA','Market_Europe','Market_LATAM','Market_Pacific Asia',
     'Market_USCA','Shipping Mode_Same Day','Shipping Mode_Second Class','Shipping Mode_Standard Class','Customer Segment','Sales']]


data = pd.concat((data_CCity,data_CState,data_OCity,data_OCountry,data_ORegion,data_OState,data_OStatus,temp),axis=1)


data


data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 180516 entries, 0 to 180518
Columns: 102 entries, CCity_1 to Sales
dtypes: float64(13), int64(75), uint8(14)
memory usage: 125.0 MB


from fast_ml.model_development import train_valid_test_split


X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(data, target = 'Sales', 
                                                                            train_size=0.7, valid_size=0.1, test_size=0.2)

print(X_train.shape), print(y_train.shape)
print(X_valid.shape), print(y_valid.shape)
print(X_test.shape), print(y_test.shape)

(126361, 101)
(126361,)
(18051, 101)
(18051,)
(36104, 101)
(36104,)

(None, None)


from sklearn.linear_model import LinearRegression


lr = LinearRegression()


lr.fit(X_train,y_train)

LinearRegression()

LinearRegression()


pred = lr.predict(X_valid)


sns.set_style('whitegrid')
plt.scatter(y_valid,pred)

<matplotlib.collections.PathCollection at 0x2180a350fa0>


def adjusted_r2_score(actual, predictions, num_pred, num_samples):
    n = num_samples
    k = num_pred
    r2 = metrics.r2_score(actual, predictions)
    adjusted_r2 = 1 - ((1-r2) * ((n-1)/(n-k-1)))
    return adjusted_r2


num_samples = X_valid.shape[0]
num_predictors = X_valid.shape[1]


from sklearn import metrics
print('Score:',lr.score(X_valid,y_valid))
print('MAE:', metrics.mean_absolute_error(y_valid,pred))
print('MSE:', metrics.mean_squared_error(y_valid,pred))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_valid,pred)))
print('R² Score:',metrics.r2_score(y_valid, pred))
print('Adjusted R² Score:',adjusted_r2_score(y_valid, pred, num_predictors, num_samples))

Score: 0.9999999998772037
MAE: 0.0005793220801803775
MSE: 2.213011052947711e-06
RMSE: 0.0014876192567144695
R² Score: 0.9999999998772037
Adjusted R² Score: 0.9999999998765127


sns.displot((y_valid - pred), bins=20)

<seaborn.axisgrid.FacetGrid at 0x2187460adf0>


sns.kdeplot((y_valid - pred))

<AxesSubplot:xlabel='Sales', ylabel='Density'>


coeff_dt = pd.DataFrame(lr.coef_,X_train.columns,columns=['Coefficient']).sort_values(by='Coefficient',ascending=False)
import pandas as pd; import numpy as np
coeff_dt = coeff_dt.reset_index()
coeff_dt


from sklearn.linear_model import Ridge


ridge = Ridge(alpha=1.0, random_state=101)


ridge.fit(X_train, y_train)

Ridge(random_state=101)

Ridge(random_state=101)


pred = ridge.predict(X_valid)


sns.set_style('whitegrid')
plt.scatter(y_valid,pred)

<matplotlib.collections.PathCollection at 0x21827d79d30>


print('Score:',ridge.score(X_valid,y_valid))
print('MAE:', metrics.mean_absolute_error(y_valid,pred))
print('MSE:', metrics.mean_squared_error(y_valid,pred))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_valid,pred)))
print('R² Score:',metrics.r2_score(y_valid, pred))
print('Adjusted R² Score:',adjusted_r2_score(y_valid, pred, num_predictors, num_samples))

Score: 0.9999999998772109
MAE: 0.0005791769984641409
MSE: 2.2128816435192384e-06
RMSE: 0.0014875757605981748
R² Score: 0.9999999998772109
Adjusted R² Score: 0.99999999987652


sns.kdeplot((y_valid - pred))

<AxesSubplot:xlabel='Sales', ylabel='Density'>


coeff_dt = pd.DataFrame(ridge.coef_,X_train.columns,columns=['Coefficient']).sort_values(by='Coefficient',ascending=False)
coeff_dt = coeff_dt.reset_index()
coeff_dt


from sklearn import linear_model


lasso = linear_model.Lasso(alpha=0.1)


lasso.fit(X_train,y_train)

Lasso(alpha=0.1)

Lasso(alpha=0.1)


pred = lasso.predict(X_valid)


sns.set_style('whitegrid')
plt.scatter(y_valid,pred)

<matplotlib.collections.PathCollection at 0x2188960b910>


print('Score:',lasso.score(X_valid,y_valid))
print('MAE:', metrics.mean_absolute_error(y_valid,pred))
print('MSE:', metrics.mean_squared_error(y_valid,pred))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_valid,pred)))
print('R² Score:',metrics.r2_score(y_valid, pred))
print('Adjusted R² Score:',adjusted_r2_score(y_valid, pred, num_predictors, num_samples))

Score: 0.9999999763087593
MAE: 0.013241754745279301
MSE: 0.0004269589791022858
RMSE: 0.02066298572574365
R² Score: 0.9999999763087593
Adjusted R² Score: 0.9999999761754473


sns.kdeplot((y_valid - pred))

<AxesSubplot:xlabel='Sales', ylabel='Density'>


coeff_dt = pd.DataFrame(lasso.coef_,X_train.columns,columns=['Coefficient']).sort_values(by='Coefficient',ascending=False)
coeff_dt = coeff_dt.reset_index()
coeff_dt


from sklearn.tree import DecisionTreeRegressor


dtr = DecisionTreeRegressor(random_state=101)


dtr.fit(X_train,y_train)

DecisionTreeRegressor(random_state=101)

DecisionTreeRegressor(random_state=101)


pred = dtr.predict(X_valid)


sns.set_style('whitegrid')
plt.scatter(y_valid,pred)

<matplotlib.collections.PathCollection at 0x21826e2cdf0>


print('Score:',dtr.score(X_valid,y_valid))
print('MAE:', metrics.mean_absolute_error(y_valid,pred))
print('MSE:', metrics.mean_squared_error(y_valid,pred))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_valid,pred)))
print('R² Score:',metrics.r2_score(y_valid, pred))
print('Adjusted R² Score:',adjusted_r2_score(y_valid, pred, num_predictors, num_samples))

Score: 0.9998913147681271
MAE: 0.031979393545458236
MSE: 1.958704318753283
RMSE: 1.399537180196826
R² Score: 0.9998913147681271
Adjusted R² Score: 0.9998907031904114


sns.kdeplot((y_valid - pred))

<AxesSubplot:xlabel='Sales', ylabel='Density'>


from sklearn.ensemble import RandomForestRegressor


rfr = RandomForestRegressor(max_depth=2, random_state=101)


rfr.fit(X_train, y_train)

RandomForestRegressor(max_depth=2, random_state=101)

RandomForestRegressor(max_depth=2, random_state=101)


pred = rfr.predict(X_valid)


sns.set_style('whitegrid')
plt.scatter(y_valid,pred)

<matplotlib.collections.PathCollection at 0x218893b14c0>


print('Score:',rfr.score(X_valid,y_valid))
print('MAE:', metrics.mean_absolute_error(y_valid,pred))
print('MSE:', metrics.mean_squared_error(y_valid,pred))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_valid,pred)))
print('R² Score:',metrics.r2_score(y_valid, pred))
print('Adjusted R² Score:',adjusted_r2_score(y_valid, pred, num_predictors, num_samples))

Score: 0.864691031284581
MAE: 38.927702238770664
MSE: 2438.512177062186
RMSE: 49.38129379696512
R² Score: 0.864691031284581
Adjusted R² Score: 0.8639296403524813


sns.kdeplot((y_valid - pred))

<AxesSubplot:xlabel='Sales', ylabel='Density'>


from sklearn import linear_model


brr = linear_model.BayesianRidge()


brr.fit(X_train,y_train)

BayesianRidge()

BayesianRidge()


pred = brr.predict(X_valid)


plt.scatter(y_valid,pred)

<matplotlib.collections.PathCollection at 0x218515082e0>


print('Score:',brr.score(X_valid,y_valid))
print('MAE:', metrics.mean_absolute_error(y_valid,pred))
print('MSE:', metrics.mean_squared_error(y_valid,pred))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_valid,pred)))
print('R² Score:',metrics.r2_score(y_valid, pred))
print('Adjusted R² Score:',adjusted_r2_score(y_valid, pred, num_predictors, num_samples))

Score: 0.9999999998772037
MAE: 0.0005793220331261513
MSE: 2.2130108041557012e-06
RMSE: 0.0014876191730936051
R² Score: 0.9999999998772037
Adjusted R² Score: 0.9999999998765127


sns.kdeplot((y_valid - pred))

<AxesSubplot:xlabel='Sales', ylabel='Density'>


coeff_dt = pd.DataFrame(brr.coef_,X_train.columns,columns=['Coefficient']).sort_values(by='Coefficient',ascending=False)
coeff_dt = coeff_dt.reset_index()
coeff_dt


X_trainValid = pd.concat([X_train,X_valid],axis=0)


X_trainValid


y_trainValid = pd.concat([y_train,y_valid],axis=0)


y_trainValid

57540     239.960007
33860      84.400002
103781    199.990005
141638    250.000000
68069     200.000000
             ...    
38178     250.000000
25905     119.980003
13855     150.000000
154978    119.970001
25774     199.949997
Name: Sales, Length: 144412, dtype: float64


model_1 = LinearRegression()
model_1.fit(X_trainValid, y_trainValid)

LinearRegression()

LinearRegression()


from sklearn.model_selection import cross_val_score


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_1, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[1. 1. 1. 1. 1.]
1.000 accuracy with a standard deviation of 0.000


pred_1 = model_1.predict(X_test)


plt.scatter(y_test,pred_1)

<matplotlib.collections.PathCollection at 0x21813850dc0>


print('Score:',model_1.score(X_test,y_test))
print('MAE:', metrics.mean_absolute_error(y_test,pred_1))
print('MSE:', metrics.mean_squared_error(y_test,pred_1))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test,pred_1)))
print('R² Score:',metrics.r2_score(y_test, pred_1))
print('Adjusted R² Score:',adjusted_r2_score(y_test, pred_1, num_predictors, num_samples))

Score: 0.9999999998691782
MAE: 0.0005754574628567571
MSE: 2.229989961786725e-06
RMSE: 0.0014933150912606239
R² Score: 0.9999999998691782
Adjusted R² Score: 0.999999999868442


sns.kdeplot((y_test - pred_1))

<AxesSubplot:xlabel='Sales', ylabel='Density'>


coeff_dt = pd.DataFrame(model_1.coef_,X_trainValid.columns,columns=['Coefficient']).sort_values(by='Coefficient',ascending=False)
coeff_dt = coeff_dt.reset_index()
coeff_dt


model_2 = Ridge(alpha=1.0, random_state=101)
model_2.fit(X_trainValid, y_trainValid)

Ridge(random_state=101)

Ridge(random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_2, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[1. 1. 1. 1. 1.]
1.000 accuracy with a standard deviation of 0.000


pred_2 = model_2.predict(X_test)


plt.scatter(y_test,pred_2)

<matplotlib.collections.PathCollection at 0x218013fe340>


print('Score:',model_2.score(X_test,y_test))
print('MAE:', metrics.mean_absolute_error(y_test,pred_2))
print('MSE:', metrics.mean_squared_error(y_test,pred_2))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test,pred_2)))
print('R² Score:',metrics.r2_score(y_test, pred_2))
print('Adjusted R² Score:',adjusted_r2_score(y_test, pred_2, num_predictors, num_samples))

Score: 0.9999999998691802
MAE: 0.0005752998545744577
MSE: 2.2299547909971216e-06
RMSE: 0.0014933033151363195
R² Score: 0.9999999998691802
Adjusted R² Score: 0.9999999998684441


sns.kdeplot((y_test - pred_2))

<AxesSubplot:xlabel='Sales', ylabel='Density'>


coeff_dt = pd.DataFrame(model_2.coef_,X_trainValid.columns,columns=['Coefficient']).sort_values(by='Coefficient',ascending=False)
coeff_dt = coeff_dt.reset_index()
coeff_dt


model_3 = linear_model.Lasso(alpha=0.1)
model_3.fit(X_trainValid, y_trainValid)

Lasso(alpha=0.1)

Lasso(alpha=0.1)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_3, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.99999998 0.99999998 0.99999998 0.99999998 0.99999998]
1.000 accuracy with a standard deviation of 0.000


pred_3 = model_3.predict(X_test)


plt.scatter(y_test,pred_3)

<matplotlib.collections.PathCollection at 0x21813832130>


print('Score:',model_3.score(X_test,y_test))
print('MAE:', metrics.mean_absolute_error(y_test,pred_3))
print('MSE:', metrics.mean_squared_error(y_test,pred_3))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test,pred_3)))
print('R² Score:',metrics.r2_score(y_test, pred_3))
print('Adjusted R² Score:',adjusted_r2_score(y_test, pred_3, num_predictors, num_samples))

Score: 0.9999999751601327
MAE: 0.01310781678071243
MSE: 0.00042342050433988077
RMSE: 0.020577184072167912
R² Score: 0.9999999751601327
Adjusted R² Score: 0.9999999750203574


sns.kdeplot((y_test - pred_3))

<AxesSubplot:xlabel='Sales', ylabel='Density'>


coeff_dt = pd.DataFrame(model_3.coef_,X_trainValid.columns,columns=['Coefficient']).sort_values(by='Coefficient',ascending=False)
coeff_dt = coeff_dt.reset_index()
coeff_dt


model_4 = DecisionTreeRegressor(random_state=101)
model_4.fit(X_trainValid, y_trainValid)

DecisionTreeRegressor(random_state=101)

DecisionTreeRegressor(random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_4, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.9999193  0.99989471 0.99862903 0.99964    0.99996857]
1.000 accuracy with a standard deviation of 0.001


pred_4 = model_4.predict(X_test)


plt.scatter(y_test,pred_4)

<matplotlib.collections.PathCollection at 0x218137e42e0>


print('Score:',model_4.score(X_test,y_test))
print('MAE:', metrics.mean_absolute_error(y_test,pred_4))
print('MSE:', metrics.mean_squared_error(y_test,pred_4))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test,pred_4)))
print('R² Score:',metrics.r2_score(y_test, pred_4))
print('Adjusted R² Score:',adjusted_r2_score(y_test, pred_4, num_predictors, num_samples))

Score: 0.9999275675150534
MAE: 0.017987206174471516
MSE: 1.234684507775159
RMSE: 1.1111635828154012
R² Score: 0.9999275675150534
Adjusted R² Score: 0.999927159933518


sns.kdeplot((y_test - pred_4))

<AxesSubplot:xlabel='Sales', ylabel='Density'>


model_5 = RandomForestRegressor(max_depth=2, random_state=101)
model_5.fit(X_trainValid, y_trainValid)

RandomForestRegressor(max_depth=2, random_state=101)

RandomForestRegressor(max_depth=2, random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_5, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.85778861 0.85760524 0.86116281 0.85670349 0.85845031]
0.858 accuracy with a standard deviation of 0.002


pred_5 = model_5.predict(X_test)


plt.scatter(y_test,pred_5)

<matplotlib.collections.PathCollection at 0x2180140d550>


print('Score:',model_5.score(X_test,y_test))
print('MAE:', metrics.mean_absolute_error(y_test,pred_5))
print('MSE:', metrics.mean_squared_error(y_test,pred_5))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test,pred_5)))
print('R² Score:',metrics.r2_score(y_test, pred_5))
print('Adjusted R² Score:',adjusted_r2_score(y_test, pred_5, num_predictors, num_samples))

Score: 0.8566078734371787
MAE: 38.964421014717935
MSE: 2444.2629206318556
RMSE: 49.43948746328036
R² Score: 0.8566078734371787
Adjusted R² Score: 0.8558009981358892


sns.kdeplot((y_test - pred_5))

<AxesSubplot:xlabel='Sales', ylabel='Density'>


model_6 = linear_model.BayesianRidge()
model_6.fit(X_trainValid, y_trainValid)

BayesianRidge()

BayesianRidge()


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_6, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[1. 1. 1. 1. 1.]
1.000 accuracy with a standard deviation of 0.000


pred_6 = model_6.predict(X_test)


plt.scatter(y_test,pred_6)

<matplotlib.collections.PathCollection at 0x218a5b6ff70>


print('Score:',model_6.score(X_test,y_test))
print('MAE:', metrics.mean_absolute_error(y_test,pred_6))
print('MSE:', metrics.mean_squared_error(y_test,pred_6))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test,pred_6)))
print('R² Score:',metrics.r2_score(y_test, pred_6))
print('Adjusted R² Score:',adjusted_r2_score(y_test, pred_6, num_predictors, num_samples))

Score: 0.9999999998691782
MAE: 0.0005754574270721686
MSE: 2.229989939164682e-06
RMSE: 0.0014933150836861865
R² Score: 0.9999999998691782
Adjusted R² Score: 0.999999999868442


sns.kdeplot((y_test - pred_6))

<AxesSubplot:xlabel='Sales', ylabel='Density'>


MLA = [model_1,model_2,model_3,model_4,model_5,model_6]


MLA_columns = []
MLA_compare = pd.DataFrame(columns = MLA_columns)

row_index = 0
for alg in MLA:  
    
    predicted = alg.fit(X_trainValid, y_trainValid).predict(X_test)
    MLA_name = alg.__class__.__name__
    MLA_compare.loc[row_index,'MLA used'] = MLA_name
    MLA_compare.loc[row_index, 'Train Accuracy'] = round(alg.score(X_trainValid, y_trainValid), 4)
    MLA_compare.loc[row_index, 'Test Accuracy'] = round(alg.score(X_test, y_test), 4)
    MLA_compare.loc[row_index, 'MAE'] = metrics.mean_absolute_error(y_test,predicted)
    MLA_compare.loc[row_index, 'MSE'] = metrics.mean_squared_error(y_test,predicted)
    MLA_compare.loc[row_index, 'RMSE'] = np.sqrt(metrics.mean_squared_error(y_test,predicted))
    MLA_compare.loc[row_index, 'R² Score'] = metrics.r2_score(y_test, predicted)
    MLA_compare.loc[row_index, 'Adjusted R² Score'] = adjusted_r2_score(y_test, predicted, num_predictors, num_samples)
    
    row_index+=1
    
MLA_compare.sort_values(by = ['Test Accuracy'], ascending = False, inplace = True)
MLA_compare


X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(data.drop(['Delivery Status_Shipping canceled',
                                                                                      'Delivery Status_Shipping on time',
                                                                                      'Delivery Status_Late delivery',
                                                                                       'Days for shipping (real)']
                                                                                      ,axis=1), target = 'Late_delivery_risk', 
                                                                            train_size=0.7, valid_size=0.1, test_size=0.2)

print(X_train.shape), print(y_train.shape)
print(X_valid.shape), print(y_valid.shape)
print(X_test.shape), print(y_test.shape)

(126361, 97)
(126361,)
(18051, 97)
(18051,)
(36104, 97)
(36104,)

(None, None)


from sklearn.tree import DecisionTreeClassifier


dt = DecisionTreeClassifier(random_state=101)


dt.fit(X_train,y_train)

DecisionTreeClassifier(random_state=101)

DecisionTreeClassifier(random_state=101)


pred = dt.predict(X_valid)


print('Score:\n',dt.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.9696969696969697 

Confusion Matrix:
 [[7795  235]
 [ 312 9709]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.96      0.97      0.97      8030
           1       0.98      0.97      0.97     10021

    accuracy                           0.97     18051
   macro avg       0.97      0.97      0.97     18051
weighted avg       0.97      0.97      0.97     18051


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


df_eval = pd.DataFrame({'Label':y_valid,
                        'Prediction': pred})
df_eval


pd.DataFrame(data=dt.feature_importances_,index=X_train.columns,columns=['Value']).sort_values(by='Value',ascending=False)


from sklearn.ensemble import RandomForestClassifier


rf = RandomForestClassifier(n_estimators=100,random_state=101)


rf.fit(X_train,y_train)

RandomForestClassifier(random_state=101)

RandomForestClassifier(random_state=101)


pred_rf = rf.predict(X_valid)


df_eval = pd.DataFrame({'Label':y_valid,
                        'Prediction': pred_rf
                        })
df_eval


print('Score:\n',rf.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred_rf),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred_rf))

Score:
 0.8997839454877846 

Confusion Matrix:
 [[7590  440]
 [1369 8652]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.85      0.95      0.89      8030
           1       0.95      0.86      0.91     10021

    accuracy                           0.90     18051
   macro avg       0.90      0.90      0.90     18051
weighted avg       0.91      0.90      0.90     18051


mat_T = confusion_matrix(y_valid,pred_rf)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


pd.DataFrame(data=rf.feature_importances_,index=X_train.columns,columns=['Value']).sort_values(by='Value',ascending=False)


from sklearn.ensemble import BaggingClassifier


bca = BaggingClassifier(random_state=101)


bca.fit(X_train, y_train)

BaggingClassifier(random_state=101)

BaggingClassifier(random_state=101)


pred = bca.predict(X_valid)


print('Score:\n',bca.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.9870921278599524 

Confusion Matrix:
 [[7962   68]
 [ 165 9856]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.98      0.99      0.99      8030
           1       0.99      0.98      0.99     10021

    accuracy                           0.99     18051
   macro avg       0.99      0.99      0.99     18051
weighted avg       0.99      0.99      0.99     18051


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True, fmt='d', cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from sklearn.ensemble import GradientBoostingClassifier


gbc = GradientBoostingClassifier(random_state=101)


gbc.fit(X_train, y_train)

GradientBoostingClassifier(random_state=101)

GradientBoostingClassifier(random_state=101)


pred = gbc.predict(X_valid)


print('Score:\n',gbc.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.9284804165974184 

Confusion Matrix:
 [[7307  723]
 [ 568 9453]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.93      0.91      0.92      8030
           1       0.93      0.94      0.94     10021

    accuracy                           0.93     18051
   macro avg       0.93      0.93      0.93     18051
weighted avg       0.93      0.93      0.93     18051


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True, fmt='d', cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from xgboost import XGBClassifier


from sklearn.metrics import accuracy_score


xgb = XGBClassifier()


xgb.fit(X_train, y_train)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)


pred = xgb.predict(X_valid)


predictions = [round(value) for value in pred]


accuracy = accuracy_score(y_valid, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))

Accuracy: 99.99%


print('Score:\n',xgb.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.9999446014071243 

Confusion Matrix:
 [[ 8029     1]
 [    0 10021]] 

Classification Report:
               precision    recall  f1-score   support

           0       1.00      1.00      1.00      8030
           1       1.00      1.00      1.00     10021

    accuracy                           1.00     18051
   macro avg       1.00      1.00      1.00     18051
weighted avg       1.00      1.00      1.00     18051


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True, fmt='d', cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


X_trainValid = pd.concat([X_train,X_valid],axis=0)


y_trainValid = pd.concat([y_train,y_valid],axis=0)


model_1 = DecisionTreeClassifier(random_state=101)
model_1.fit(X_trainValid, y_trainValid)

DecisionTreeClassifier(random_state=101)

DecisionTreeClassifier(random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_1, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.95951436 0.94395716 0.95824485 0.95623673 0.96756994]
0.957 accuracy with a standard deviation of 0.008


pred_1 = model_1.predict(X_test)


print('Score:\n',model_1.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_1),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_1))

Score:
 0.9630511854642145 

Confusion Matrix:
 [[15642   677]
 [  657 19128]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.96      0.96      0.96     16319
           1       0.97      0.97      0.97     19785

    accuracy                           0.96     36104
   macro avg       0.96      0.96      0.96     36104
weighted avg       0.96      0.96      0.96     36104


model_2 = RandomForestClassifier(n_estimators=100,random_state=101)
model_2.fit(X_trainValid, y_trainValid)

RandomForestClassifier(random_state=101)

RandomForestClassifier(random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_2, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.86737143 0.87727357 0.87055674 0.87298033 0.87388053]
0.872 accuracy with a standard deviation of 0.003


pred_2 = model_2.predict(X_test)


print('Score:\n',model_2.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_2),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_2))

Score:
 0.9199811655218258 

Confusion Matrix:
 [[15519   800]
 [ 2089 17696]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.88      0.95      0.91     16319
           1       0.96      0.89      0.92     19785

    accuracy                           0.92     36104
   macro avg       0.92      0.92      0.92     36104
weighted avg       0.92      0.92      0.92     36104


model_3 = BaggingClassifier()
model_3.fit(X_trainValid, y_trainValid)

BaggingClassifier()

BaggingClassifier()


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_3, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.97592558 0.98008032 0.97627181 0.96752377 0.98813591]
0.978 accuracy with a standard deviation of 0.007


pred_3 = model_3.predict(X_test)


print('Score:\n',model_3.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_3),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_3))

Score:
 0.9904165743407932 

Confusion Matrix:
 [[16216   103]
 [  243 19542]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.99      0.99      0.99     16319
           1       0.99      0.99      0.99     19785

    accuracy                           0.99     36104
   macro avg       0.99      0.99      0.99     36104
weighted avg       0.99      0.99      0.99     36104


model_4 = GradientBoostingClassifier(random_state=101)
model_4.fit(X_trainValid, y_trainValid)

GradientBoostingClassifier(random_state=101)

GradientBoostingClassifier(random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_4, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.92463761 0.91969809 0.92496076 0.90820331 0.92410673]
0.920 accuracy with a standard deviation of 0.006


pred_4 = model_4.predict(X_test)


print('Score:\n',model_4.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_4),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_4))

Score:
 0.9181254154664303 

Confusion Matrix:
 [[14803  1516]
 [ 1440 18345]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.91      0.91      0.91     16319
           1       0.92      0.93      0.93     19785

    accuracy                           0.92     36104
   macro avg       0.92      0.92      0.92     36104
weighted avg       0.92      0.92      0.92     36104


model_5 = XGBClassifier()
model_5.fit(X_trainValid, y_trainValid)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_5, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[1.         0.99983843 0.99993075 0.99993075 0.99993075]
1.000 accuracy with a standard deviation of 0.000


pred_5 = model_5.predict(X_test)


print('Score:\n',model_5.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_5),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_5))

Score:
 0.9999446044759583 

Confusion Matrix:
 [[16317     2]
 [    0 19785]] 

Classification Report:
               precision    recall  f1-score   support

           0       1.00      1.00      1.00     16319
           1       1.00      1.00      1.00     19785

    accuracy                           1.00     36104
   macro avg       1.00      1.00      1.00     36104
weighted avg       1.00      1.00      1.00     36104


from sklearn.metrics import roc_curve
from sklearn.metrics import auc
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score


MLA = [model_1,model_2,model_3,model_4,model_5]


MLA_columns = []
MLA_compare = pd.DataFrame(columns = MLA_columns)

row_index = 0
for alg in MLA:  
    
    predicted = alg.fit(X_trainValid, y_trainValid).predict(X_test)
    fp, tp, th = roc_curve(y_test, predicted)
    MLA_name = alg.__class__.__name__
    MLA_compare.loc[row_index,'MLA used'] = MLA_name
    MLA_compare.loc[row_index, 'Train Accuracy'] = round(alg.score(X_trainValid, y_trainValid), 4)
    MLA_compare.loc[row_index, 'Test Accuracy'] = round(alg.score(X_test, y_test), 4)
    MLA_compare.loc[row_index, 'Precission'] = precision_score(y_test, predicted)
    MLA_compare.loc[row_index, 'Recall'] = recall_score(y_test, predicted)
    MLA_compare.loc[row_index, 'AUC'] = auc(fp, tp)
    MLA_compare.loc[row_index, 'F1-Score'] = f1_score(y_test, predicted)
    
    row_index+=1
    
MLA_compare.sort_values(by = ['Test Accuracy'], ascending = False, inplace = True)
MLA_compare


# Creating plot to show the ROC for all MLA
index = 1
for alg in MLA:
    
    
    predicted = alg.fit(X_trainValid, y_trainValid).predict(X_test)
    fp, tp, th = roc_curve(y_test, predicted)
    roc_auc_mla = auc(fp, tp)
    MLA_name = alg.__class__.__name__
    plt.plot(fp, tp, lw=2, alpha=0.3, label='ROC %s (AUC = %0.2f)'  % (MLA_name, roc_auc_mla))
   
    index+=1

plt.title('ROC Curve comparison')
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.plot([0,1],[0,1],'r--')
plt.xlim([0,1])
plt.ylim([0,1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')    
plt.show()

	Days for shipping (real)	Days for shipment (scheduled)	Benefit per order	Sales per customer	Late_delivery_risk	Category Id	Customer Id	Customer Zipcode	Department Id	Latitude	...	Order Item Quantity	Sales	Order Item Total	Order Profit Per Order	Order Zipcode	Product Card Id	Product Category Id	Product Description	Product Price	Product Status
count	180519.000000	180519.000000	180519.000000	180519.000000	180519.000000	180519.000000	180519.000000	180516.000000	180519.000000	180519.000000	...	180519.000000	180519.000000	180519.000000	180519.000000	24840.000000	180519.000000	180519.000000	0.0	180519.000000	180519.0
mean	3.497654	2.931847	21.974989	183.107609	0.548291	31.851451	6691.379495	35921.126914	5.443460	29.719955	...	2.127638	203.772096	183.107609	21.974989	55426.132327	692.509764	31.851451	NaN	141.232550	0.0
std	1.623722	1.374449	104.433526	120.043670	0.497664	15.640064	4162.918106	37542.461122	1.629246	9.813646	...	1.453451	132.273077	120.043670	104.433526	31919.279101	336.446807	15.640064	NaN	139.732492	0.0
min	0.000000	0.000000	-4274.979980	7.490000	0.000000	2.000000	1.000000	603.000000	2.000000	-33.937553	...	1.000000	9.990000	7.490000	-4274.979980	1040.000000	19.000000	2.000000	NaN	9.990000	0.0
25%	2.000000	2.000000	7.000000	104.379997	0.000000	18.000000	3258.500000	725.000000	4.000000	18.265432	...	1.000000	119.980003	104.379997	7.000000	23464.000000	403.000000	18.000000	NaN	50.000000	0.0
50%	3.000000	4.000000	31.520000	163.990005	1.000000	29.000000	6457.000000	19380.000000	5.000000	33.144863	...	1.000000	199.919998	163.990005	31.520000	59405.000000	627.000000	29.000000	NaN	59.990002	0.0
75%	5.000000	4.000000	64.800003	247.399994	1.000000	45.000000	9779.000000	78207.000000	7.000000	39.279617	...	3.000000	299.950012	247.399994	64.800003	90008.000000	1004.000000	45.000000	NaN	199.990005	0.0
max	6.000000	4.000000	911.799988	1939.989990	1.000000	76.000000	20757.000000	99205.000000	12.000000	48.781933	...	5.000000	1999.989990	1939.989990	911.799988	99301.000000	1363.000000	76.000000	NaN	1999.989990	0.0

	Days for shipping (real)	Days for shipment (scheduled)	Benefit per order	Sales per customer	Late_delivery_risk	Category Id	Customer Id	Customer Zipcode	Department Id	Latitude	...	DayOrders	MonthOrders	YearOrders	HourOrders	MinutesOrders	shipping Day	shipping Month	shipping Year	shipping Hour	shipping Minute
Days for shipping (real)	1.000000	0.515876	-0.005100	0.001757	0.401428	-0.000345	0.003434	0.000112	-0.001619	-0.004071	...	-0.000485	-0.007591	0.000844	0.016692	-0.005868	-0.004163	-0.005037	0.005416	-0.019609	-0.005868
Days for shipment (scheduled)	0.515876	1.000000	-0.000183	0.006446	-0.369342	-0.000371	0.000895	-0.003019	-0.000246	-0.005299	...	-0.000864	-0.007460	0.000655	-0.000855	-0.007037	-0.008132	-0.006173	0.003175	-0.009525	-0.007037
Benefit per order	-0.005100	-0.000183	1.000000	0.133482	-0.003728	0.031883	0.009348	0.002235	0.031439	0.000336	...	0.003492	0.009060	0.010698	-0.000396	-0.002252	0.002701	0.009357	0.010562	0.001562	-0.002252
Sales per customer	0.001757	0.006446	0.133482	1.000000	-0.003793	0.225220	0.060190	-0.001304	0.232396	-0.000219	...	0.035584	0.056923	0.059583	-0.001777	0.002003	0.006602	0.055819	0.060638	-0.000050	0.002003
Late_delivery_risk	0.401428	-0.369342	-0.003728	-0.003793	1.000000	0.001770	0.001501	0.003148	0.001071	0.000682	...	-0.002017	0.002597	-0.002172	0.047035	0.000423	-0.000371	0.003698	-0.000439	-0.043403	0.000423
Category Id	-0.000345	-0.000371	0.031883	0.225220	0.001770	1.000000	0.274575	0.002898	0.888851	0.002165	...	0.003528	0.062401	0.179608	0.001340	0.004345	0.003488	0.053554	0.182089	-0.000113	0.004345
Customer Id	0.003434	0.000895	0.009348	0.060190	0.001501	0.274575	1.000000	0.002022	0.136856	0.002308	...	0.011786	0.050909	0.189726	-0.001525	0.003676	0.004971	0.040855	0.192796	-0.001038	0.003676
Customer Zipcode	0.000112	-0.003019	0.002235	-0.001304	0.003148	0.002898	0.002022	1.000000	0.002878	0.584553	...	0.006988	-0.000686	-0.001212	0.005379	0.002974	0.001986	-0.001540	-0.000774	0.000843	0.002974
Department Id	-0.001619	-0.000246	0.031439	0.232396	0.001071	0.888851	0.136856	0.002878	1.000000	0.002437	...	-0.004422	0.037399	0.095469	-0.000356	0.004123	-0.004092	0.035109	0.095956	-0.001397	0.004123
Latitude	-0.004071	-0.005299	0.000336	-0.000219	0.000682	0.002165	0.002308	0.584553	0.002437	1.000000	...	0.006963	-0.004353	-0.001774	0.001128	-0.001352	-0.001668	-0.004465	-0.001511	-0.000523	-0.001352
Longitude	0.003907	0.003675	-0.002514	0.001441	-0.001926	-0.002256	-0.005088	-0.923921	-0.002394	-0.525116	...	-0.007882	0.003016	0.001835	-0.005534	-0.005523	-0.001755	0.003521	0.001503	-0.000278	-0.005523
Order Customer Id	0.003434	0.000895	0.009348	0.060190	0.001501	0.274575	1.000000	0.002022	0.136856	0.002308	...	0.011786	0.050909	0.189726	-0.001525	0.003676	0.004971	0.040855	0.192796	-0.001038	0.003676
Order Id	-0.001708	-0.001880	0.013711	0.079005	-0.001280	0.198445	0.204776	-0.001210	0.106725	-0.003006	...	0.027709	0.206548	0.941951	-0.001397	-0.006346	0.013713	0.198371	0.942353	-0.005241	-0.006346
Order Item Cardprod Id	-0.000856	-0.000433	0.033155	0.236379	0.001504	0.991093	0.227987	0.003173	0.906135	0.002006	...	0.001572	0.060483	0.144787	0.001100	0.004486	0.003285	0.052103	0.147163	-0.000233	0.004486
Order Item Discount	0.002240	0.002909	0.064749	0.498732	-0.000755	0.140162	0.038469	-0.001861	0.144614	-0.003007	...	0.024532	0.035658	0.037131	-0.002404	0.005123	0.003450	0.034976	0.037881	-0.001372	0.005123
Order Item Discount Rate	0.001478	0.000083	-0.018652	-0.119485	0.000392	0.000556	0.001698	-0.000135	0.000241	-0.003896	...	0.001257	0.001097	0.000065	-0.001567	0.003694	0.000035	0.001015	0.000137	-0.001441	0.003694
Order Item Id	-0.001767	-0.001850	0.013550	0.078180	-0.001364	0.186746	0.191770	-0.001208	0.101645	-0.003079	...	0.026997	0.207374	0.941312	-0.001427	-0.006422	0.013365	0.199718	0.941528	-0.005258	-0.006422
Order Item Product Price	0.002188	0.006915	0.103455	0.781781	-0.002174	0.461068	0.111110	0.002191	0.446768	0.000467	...	0.033883	0.070341	0.091834	-0.000600	0.004369	0.005314	0.067137	0.093500	0.000201	0.004369
Order Item Profit Ratio	-0.004636	-0.001820	0.823690	-0.001438	-0.002315	-0.001627	-0.000348	0.003364	-0.003055	-0.000086	...	-0.001667	0.001919	0.002178	0.001311	-0.000096	-0.000418	0.001951	0.002098	0.002618	-0.000096
Order Item Quantity	-0.000812	-0.002924	0.015699	0.105413	-0.000145	-0.302394	-0.091305	-0.006174	-0.265430	-0.001843	...	0.000103	-0.029217	-0.078237	-0.002126	-0.001833	0.002318	-0.025921	-0.079200	-0.001228	-0.001833
Sales	0.001964	0.006330	0.131813	0.989744	-0.003567	0.227497	0.060965	-0.001490	0.234744	-0.000695	...	0.036337	0.057537	0.060194	-0.002009	0.002662	0.006560	0.056423	0.061275	-0.000272	0.002662
Order Item Total	0.001757	0.006446	0.133482	1.000000	-0.003793	0.225220	0.060190	-0.001304	0.232396	-0.000219	...	0.035584	0.056923	0.059583	-0.001777	0.002003	0.006602	0.055819	0.060638	-0.000050	0.002003
Order Profit Per Order	-0.005100	-0.000183	1.000000	0.133482	-0.003728	0.031883	0.009348	0.002235	0.031439	0.000336	...	0.003492	0.009060	0.010698	-0.000396	-0.002252	0.002701	0.009357	0.010562	0.001562	-0.002252
Product Card Id	-0.000856	-0.000433	0.033155	0.236379	0.001504	0.991093	0.227987	0.003173	0.906135	0.002006	...	0.001572	0.060483	0.144787	0.001100	0.004486	0.003285	0.052103	0.147163	-0.000233	0.004486
Product Category Id	-0.000345	-0.000371	0.031883	0.225220	0.001770	1.000000	0.274575	0.002898	0.888851	0.002165	...	0.003528	0.062401	0.179608	0.001340	0.004345	0.003488	0.053554	0.182089	-0.000113	0.004345
Product Price	0.002188	0.006915	0.103455	0.781781	-0.002174	0.461068	0.111110	0.002191	0.446768	0.000467	...	0.033883	0.070341	0.091834	-0.000600	0.004369	0.005314	0.067137	0.093500	0.000201	0.004369
DayOrders	-0.000485	-0.000864	0.003492	0.035584	-0.002017	0.003528	0.011786	0.006988	-0.004422	0.006963	...	1.000000	0.008231	-0.003786	0.001723	-0.001901	0.405038	0.013916	0.011548	0.002343	-0.001901
MonthOrders	-0.007591	-0.007460	0.009060	0.056923	0.002597	0.062401	0.050909	-0.000686	0.037399	-0.004353	...	0.008231	1.000000	-0.132711	0.001248	0.003251	0.004368	0.952177	-0.115503	0.003443	0.003251
YearOrders	0.000844	0.000655	0.010698	0.059583	-0.002172	0.179608	0.189726	-0.001212	0.095469	-0.001774	...	-0.003786	-0.132711	1.000000	-0.002853	-0.007518	0.000655	-0.124803	0.994073	-0.007439	-0.007518
HourOrders	0.016692	-0.000855	-0.000396	-0.001777	0.047035	0.001340	-0.001525	0.005379	-0.000356	0.001128	...	0.001723	0.001248	-0.002853	1.000000	0.000694	0.004149	0.000257	-0.002483	0.918931	0.000694
MinutesOrders	-0.005868	-0.007037	-0.002252	0.002003	0.000423	0.004345	0.003676	0.002974	0.004123	-0.001352	...	-0.001901	0.003251	-0.007518	0.000694	1.000000	0.000871	0.002093	-0.007211	0.002628	1.000000
shipping Day	-0.004163	-0.008132	0.002701	0.006602	-0.000371	0.003488	0.004971	0.001986	-0.004092	-0.001668	...	0.405038	0.004368	0.000655	0.004149	0.000871	1.000000	-0.002972	-0.014092	0.004270	0.000871
shipping Month	-0.005037	-0.006173	0.009357	0.055819	0.003698	0.053554	0.040855	-0.001540	0.035109	-0.004465	...	0.013916	0.952177	-0.124803	0.000257	0.002093	-0.002972	1.000000	-0.139757	0.002073	0.002093
shipping Year	0.005416	0.003175	0.010562	0.060638	-0.000439	0.182089	0.192796	-0.000774	0.095956	-0.001511	...	0.011548	-0.115503	0.994073	-0.002483	-0.007211	-0.014092	-0.139757	1.000000	-0.007107	-0.007211
shipping Hour	-0.019609	-0.009525	0.001562	-0.000050	-0.043403	-0.000113	-0.001038	0.000843	-0.001397	-0.000523	...	0.002343	0.003443	-0.007439	0.918931	0.002628	0.004270	0.002073	-0.007107	1.000000	0.002628
shipping Minute	-0.005868	-0.007037	-0.002252	0.002003	0.000423	0.004345	0.003676	0.002974	0.004123	-0.001352	...	-0.001901	0.003251	-0.007518	0.000694	1.000000	0.000871	0.002093	-0.007211	0.002628	1.000000

	support	itemsets
5	0.615696	(Customer Country_USA)
9	0.596900	(Shipping Mode_Standard Class)
10	0.548295	(Delivery Status_Late delivery, Late_delivery_...
0	0.548295	(Late_delivery_risk)
2	0.548295	(Delivery Status_Late delivery)
6	0.517971	(Customer Segment_Consumer)
4	0.384304	(Customer Country_Puerto Rico)
1	0.383861	(Type_DEBIT)
3	0.369884	(Customer City_Caguas)
14	0.369884	(Customer City_Caguas, Customer Country_Puerto...
16	0.366433	(Shipping Mode_Standard Class, Customer Countr...
11	0.337837	(Late_delivery_risk, Customer Country_USA)
13	0.337837	(Delivery Status_Late delivery, Customer Count...
18	0.337837	(Delivery Status_Late delivery, Late_delivery_...
8	0.329550	(Order Status_COMPLETE)
12	0.329550	(Order Status_COMPLETE, Type_DEBIT)
15	0.320348	(Customer Segment_Consumer, Customer Country_USA)
17	0.309950	(Customer Segment_Consumer, Shipping Mode_Stan...
7	0.303508	(Customer Segment_Corporate)

	antecedents	consequents	antecedent support	consequent support	support	confidence	lift	leverage	conviction	zhangs_metric
0	(Delivery Status_Late delivery)	(Late_delivery_risk)	0.548295	0.548295	0.548295	1.000000	1.823836	0.247668	inf	1.000000
1	(Late_delivery_risk)	(Delivery Status_Late delivery)	0.548295	0.548295	0.548295	1.000000	1.823836	0.247668	inf	1.000000
2	(Late_delivery_risk)	(Customer Country_USA)	0.548295	0.615696	0.337837	0.616159	1.000753	0.000254	1.001207	0.001665
3	(Customer Country_USA)	(Late_delivery_risk)	0.615696	0.548295	0.337837	0.548708	1.000753	0.000254	1.000914	0.001957
4	(Order Status_COMPLETE)	(Type_DEBIT)	0.329550	0.383861	0.329550	1.000000	2.605112	0.203049	inf	0.918993
5	(Type_DEBIT)	(Order Status_COMPLETE)	0.383861	0.329550	0.329550	0.858514	2.605112	0.203049	4.738628	1.000000
6	(Delivery Status_Late delivery)	(Customer Country_USA)	0.548295	0.615696	0.337837	0.616159	1.000753	0.000254	1.001207	0.001665
7	(Customer Country_USA)	(Delivery Status_Late delivery)	0.615696	0.548295	0.337837	0.548708	1.000753	0.000254	1.000914	0.001957
8	(Customer City_Caguas)	(Customer Country_Puerto Rico)	0.369884	0.384304	0.369884	1.000000	2.602107	0.227736	inf	0.977116
9	(Customer Country_Puerto Rico)	(Customer City_Caguas)	0.384304	0.369884	0.369884	0.962478	2.602107	0.227736	16.793327	1.000000
10	(Customer Segment_Consumer)	(Customer Country_USA)	0.517971	0.615696	0.320348	0.618468	1.004502	0.001436	1.007265	0.009298
11	(Customer Country_USA)	(Customer Segment_Consumer)	0.615696	0.517971	0.320348	0.520303	1.004502	0.001436	1.004861	0.011662
12	(Shipping Mode_Standard Class)	(Customer Country_USA)	0.596900	0.615696	0.366433	0.613893	0.997072	-0.001076	0.995331	-0.007233
13	(Customer Country_USA)	(Shipping Mode_Standard Class)	0.615696	0.596900	0.366433	0.595152	0.997072	-0.001076	0.995683	-0.007584
14	(Customer Segment_Consumer)	(Shipping Mode_Standard Class)	0.517971	0.596900	0.309950	0.598394	1.002502	0.000774	1.003719	0.005178
15	(Shipping Mode_Standard Class)	(Customer Segment_Consumer)	0.596900	0.517971	0.309950	0.519267	1.002502	0.000774	1.002696	0.006192
16	(Delivery Status_Late delivery, Late_delivery_...	(Customer Country_USA)	0.548295	0.615696	0.337837	0.616159	1.000753	0.000254	1.001207	0.001665
17	(Delivery Status_Late delivery, Customer Count...	(Late_delivery_risk)	0.337837	0.548295	0.337837	1.000000	1.823836	0.152603	inf	0.682166
18	(Late_delivery_risk, Customer Country_USA)	(Delivery Status_Late delivery)	0.337837	0.548295	0.337837	1.000000	1.823836	0.152603	inf	0.682166
19	(Delivery Status_Late delivery)	(Late_delivery_risk, Customer Country_USA)	0.548295	0.337837	0.337837	0.616159	1.823836	0.152603	1.725099	1.000000

	antecedents	consequents	antecedent support	consequent support	support	confidence	lift	leverage	conviction	zhangs_metric
4	(Order Status_COMPLETE)	(Type_DEBIT)	0.329550	0.383861	0.329550	1.000000	2.605112	0.203049	inf	0.918993
5	(Type_DEBIT)	(Order Status_COMPLETE)	0.383861	0.329550	0.329550	0.858514	2.605112	0.203049	4.738628	1.000000
8	(Customer City_Caguas)	(Customer Country_Puerto Rico)	0.369884	0.384304	0.369884	1.000000	2.602107	0.227736	inf	0.977116
9	(Customer Country_Puerto Rico)	(Customer City_Caguas)	0.384304	0.369884	0.369884	0.962478	2.602107	0.227736	16.793327	1.000000
0	(Delivery Status_Late delivery)	(Late_delivery_risk)	0.548295	0.548295	0.548295	1.000000	1.823836	0.247668	inf	1.000000
20	(Late_delivery_risk)	(Delivery Status_Late delivery, Customer Count...	0.548295	0.337837	0.337837	0.616159	1.823836	0.152603	1.725099	1.000000
19	(Delivery Status_Late delivery)	(Late_delivery_risk, Customer Country_USA)	0.548295	0.337837	0.337837	0.616159	1.823836	0.152603	1.725099	1.000000
18	(Late_delivery_risk, Customer Country_USA)	(Delivery Status_Late delivery)	0.337837	0.548295	0.337837	1.000000	1.823836	0.152603	inf	0.682166
17	(Delivery Status_Late delivery, Customer Count...	(Late_delivery_risk)	0.337837	0.548295	0.337837	1.000000	1.823836	0.152603	inf	0.682166
1	(Late_delivery_risk)	(Delivery Status_Late delivery)	0.548295	0.548295	0.548295	1.000000	1.823836	0.247668	inf	1.000000
11	(Customer Country_USA)	(Customer Segment_Consumer)	0.615696	0.517971	0.320348	0.520303	1.004502	0.001436	1.004861	0.011662
10	(Customer Segment_Consumer)	(Customer Country_USA)	0.517971	0.615696	0.320348	0.618468	1.004502	0.001436	1.007265	0.009298
14	(Customer Segment_Consumer)	(Shipping Mode_Standard Class)	0.517971	0.596900	0.309950	0.598394	1.002502	0.000774	1.003719	0.005178
15	(Shipping Mode_Standard Class)	(Customer Segment_Consumer)	0.596900	0.517971	0.309950	0.519267	1.002502	0.000774	1.002696	0.006192
16	(Delivery Status_Late delivery, Late_delivery_...	(Customer Country_USA)	0.548295	0.615696	0.337837	0.616159	1.000753	0.000254	1.001207	0.001665
7	(Customer Country_USA)	(Delivery Status_Late delivery)	0.615696	0.548295	0.337837	0.548708	1.000753	0.000254	1.000914	0.001957
6	(Delivery Status_Late delivery)	(Customer Country_USA)	0.548295	0.615696	0.337837	0.616159	1.000753	0.000254	1.001207	0.001665
3	(Customer Country_USA)	(Late_delivery_risk)	0.615696	0.548295	0.337837	0.548708	1.000753	0.000254	1.000914	0.001957
2	(Late_delivery_risk)	(Customer Country_USA)	0.548295	0.615696	0.337837	0.616159	1.000753	0.000254	1.001207	0.001665
21	(Customer Country_USA)	(Delivery Status_Late delivery, Late_delivery_...	0.615696	0.548295	0.337837	0.548708	1.000753	0.000254	1.000914	0.001957

	Type	Days for shipping (real)	Days for shipment (scheduled)	Benefit per order	Sales per customer	Delivery Status	Late_delivery_risk	Category Id	Category Name	Customer City	...	Order Zipcode	Product Card Id	Product Category Id	Product Description	Product Image	Product Name	Product Price	shipping date (DateOrders)	Shipping Mode
0	DEBIT	3	4	91.250000	314.640015	Advance shipping	0	73	Sporting Goods	Caguas	...	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	2/3/2018 22:56	Standard Class
1	TRANSFER	5	4	-249.089996	311.359985	Late delivery	1	73	Sporting Goods	Caguas	...	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	1/18/2018 12:27	Standard Class
2	CASH	4	4	-247.779999	309.720001	Shipping on time	0	73	Sporting Goods	San Jose	...	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	1/17/2018 12:06	Standard Class
3	DEBIT	3	4	22.860001	304.809998	Advance shipping	0	73	Sporting Goods	Los Angeles	...	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	1/16/2018 11:45	Standard Class
4	PAYMENT	2	4	134.210007	298.250000	Advance shipping	0	73	Sporting Goods	Caguas	...	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	1/15/2018 11:24	Standard Class

	Type	Days for shipping (real)	Days for shipment (scheduled)	Benefit per order	Sales per customer	Delivery Status	Late_delivery_risk	Category Id	Category Name	Customer City	...	Order Zipcode	Product Card Id	Product Category Id	Product Description	Product Image	Product Name	Product Price	shipping date (DateOrders)	Shipping Mode
180514	CASH	4	4	40.000000	399.980011	Shipping on time	0	45	Fishing	Brooklyn	...	NaN	1004	45	NaN	http://images.acmesports.sports/Field+%26+Stre...	Field & Stream Sportsman 16 Gun Fire Safe	399.980011	1/20/2016 3:40	Standard Class
180515	DEBIT	3	2	-613.770019	395.980011	Late delivery	1	45	Fishing	Bakersfield	...	NaN	1004	45	NaN	http://images.acmesports.sports/Field+%26+Stre...	Field & Stream Sportsman 16 Gun Fire Safe	399.980011	1/19/2016 1:34	Second Class
180516	TRANSFER	5	4	141.110001	391.980011	Late delivery	1	45	Fishing	Bristol	...	NaN	1004	45	NaN	http://images.acmesports.sports/Field+%26+Stre...	Field & Stream Sportsman 16 Gun Fire Safe	399.980011	1/20/2016 21:00	Standard Class
180517	PAYMENT	3	4	186.229996	387.980011	Advance shipping	0	45	Fishing	Caguas	...	NaN	1004	45	NaN	http://images.acmesports.sports/Field+%26+Stre...	Field & Stream Sportsman 16 Gun Fire Safe	399.980011	1/18/2016 20:18	Standard Class
180518	PAYMENT	4	4	168.949997	383.980011	Shipping on time	0	45	Fishing	Caguas	...	NaN	1004	45	NaN	http://images.acmesports.sports/Field+%26+Stre...	Field & Stream Sportsman 16 Gun Fire Safe	399.980011	1/19/2016 18:54	Standard Class

	Type	Days for shipping (real)	Days for shipment (scheduled)	Benefit per order	Sales per customer	Delivery Status	Late_delivery_risk	Category Id	Category Name	Customer City	...	Order Zipcode	Product Card Id	Product Category Id	Product Description	Product Image	Product Name	Product Price	shipping date (DateOrders)	Shipping Mode
47045	PAYMENT	2	4	6.910000	115.180000	Advance shipping	0	17	Cleats	Waukegan	...	23434.0	365	17	NaN	http://images.acmesports.sports/Perfect+Fitnes...	Perfect Fitness Perfect Rip Deck	59.990002	7/25/2016 2:10	Standard Class
114147	CASH	5	4	36.950001	131.949997	Late delivery	1	46	Indoor/Outdoor Games	Westminster	...	NaN	1014	46	NaN	http://images.acmesports.sports/O%27Brien+Men%...	O'Brien Men's Neoprene Life Vest	49.980000	2/14/2015 17:59	Standard Class
128376	DEBIT	5	4	85.250000	293.970001	Late delivery	1	9	Cardio Equipment	Jacksonville	...	NaN	191	9	NaN	http://images.acmesports.sports/Nike+Men%27s+F...	Nike Men's Free 5.0+ Running Shoe	99.989998	10/8/2015 13:48	Standard Class
127830	DEBIT	5	4	45.000000	149.990005	Late delivery	1	9	Cardio Equipment	Hanford	...	NaN	191	9	NaN	http://images.acmesports.sports/Nike+Men%27s+F...	Nike Men's Free 5.0+ Running Shoe	99.989998	2/3/2017 20:29	Standard Class
40131	DEBIT	6	2	-325.940002	424.959992	Late delivery	1	9	Cardio Equipment	Caguas	...	NaN	191	9	NaN	http://images.acmesports.sports/Nike+Men%27s+F...	Nike Men's Free 5.0+ Running Shoe	99.989998	6/27/2017 2:56	Second Class

	Type	Days for shipping (real)	Days for shipment (scheduled)	Benefit per order	Sales per customer	Delivery Status	Late_delivery_risk	Category Id	Category Name	Customer City	...	Order Zipcode	Product Card Id	Product Category Id	Product Description	Product Image	Product Name	Product Price	shipping date (DateOrders)	Shipping Mode
6224	PAYMENT	6	4	0.460000	41.500000	Late delivery	1	24	Women's Apparel	Caguas	...	NaN	502	24	NaN	http://images.acmesports.sports/Nike+Men%27s+D...	Nike Men's Dri-FIT Victory Golf Polo	50.000000	1/19/2017 3:41	Standard Class
6754	PAYMENT	5	4	12.600000	200.000000	Late delivery	1	24	Women's Apparel	Caguas	...	NaN	502	24	NaN	http://images.acmesports.sports/Nike+Men%27s+D...	Nike Men's Dri-FIT Victory Golf Polo	50.000000	8/19/2017 18:37	Standard Class
6912	PAYMENT	5	4	100.089996	454.950012	Late delivery	1	9	Cardio Equipment	Caguas	...	NaN	191	9	NaN	http://images.acmesports.sports/Nike+Men%27s+F...	Nike Men's Free 5.0+ Running Shoe	99.989998	8/19/2017 18:37	Standard Class
10652	PAYMENT	6	4	90.660004	278.950012	Late delivery	1	17	Cleats	Caguas	...	NaN	365	17	NaN	http://images.acmesports.sports/Perfect+Fitnes...	Perfect Fitness Perfect Rip Deck	59.990002	1/19/2017 3:41	Standard Class
10667	PAYMENT	6	4	-37.750000	173.960007	Late delivery	1	29	Shop By Sport	Caguas	...	NaN	627	29	NaN	http://images.acmesports.sports/Under+Armour+G...	Under Armour Girls' Toddler Spine Surge Runni	39.990002	1/19/2017 3:41	Standard Class
10795	TRANSFER	2	1	37.750000	114.389999	Late delivery	1	18	Men's Footwear	Caguas	...	NaN	403	18	NaN	http://images.acmesports.sports/Nike+Men%27s+C...	Nike Men's CJ Elite 2 TD Football Cleat	129.990005	3/24/2017 22:51	First Class
11075	TRANSFER	2	1	52.840000	145.559998	Late delivery	1	29	Shop By Sport	Caguas	...	NaN	627	29	NaN	http://images.acmesports.sports/Under+Armour+G...	Under Armour Girls' Toddler Spine Surge Runni	39.990002	3/24/2017 22:51	First Class
11497	TRANSFER	5	2	24.090000	113.089996	Late delivery	1	18	Men's Footwear	Caguas	...	88220.0	403	18	NaN	http://images.acmesports.sports/Nike+Men%27s+C...	Nike Men's CJ Elite 2 TD Football Cleat	129.990005	5/16/2016 8:45	Second Class
11624	TRANSFER	5	2	40.570000	88.199997	Late delivery	1	9	Cardio Equipment	Caguas	...	88220.0	172	9	NaN	http://images.acmesports.sports/Nike+Women%27s...	Nike Women's Tempo Shorts	30.000000	5/16/2016 8:45	Second Class
11736	TRANSFER	5	2	-11.280000	296.950012	Late delivery	1	17	Cleats	Caguas	...	88220.0	365	17	NaN	http://images.acmesports.sports/Perfect+Fitnes...	Perfect Fitness Perfect Rip Deck	59.990002	5/16/2016 8:45	Second Class
30223	TRANSFER	6	4	8.010000	90.989998	Late delivery	1	9	Cardio Equipment	Caguas	...	NaN	191	9	NaN	http://images.acmesports.sports/Nike+Men%27s+F...	Nike Men's Free 5.0+ Running Shoe	99.989998	3/17/2015 0:46	Standard Class
30236	TRANSFER	6	4	5.120000	58.189999	Late delivery	1	17	Cleats	Caguas	...	NaN	365	17	NaN	http://images.acmesports.sports/Perfect+Fitnes...	Perfect Fitness Perfect Rip Deck	59.990002	3/17/2015 0:46	Standard Class
33028	TRANSFER	5	2	30.620001	105.570000	Late delivery	1	35	Golf Gloves	Caguas	...	88220.0	771	35	NaN	http://images.acmesports.sports/Clicgear+Rovic...	Clicgear Rovic Cooler Bag	39.990002	5/16/2016 8:45	Second Class
51532	DEBIT	3	2	14.270000	50.970001	Late delivery	1	32	Golf Apparel	Caguas	...	NaN	703	32	NaN	http://images.acmesports.sports/Top+Flite+Wome...	Top Flite Women's 2014 XL Hybrid	19.990000	2/27/2016 9:44	Second Class
55736	TRANSFER	2	1	56.840000	195.990005	Late delivery	1	48	Water Sports	Caguas	...	NaN	1073	48	NaN	http://images.acmesports.sports/Pelican+Sunstr...	Pelican Sunstream 100 Kayak	199.990005	3/24/2017 22:51	First Class
61162	DEBIT	4	4	-14.780000	167.990005	Shipping on time	0	48	Water Sports	Caguas	...	NaN	1073	48	NaN	http://images.acmesports.sports/Pelican+Sunstr...	Pelican Sunstream 100 Kayak	199.990005	6/25/2015 16:45	Standard Class
73198	CASH	4	2	89.300003	189.990005	Late delivery	1	48	Water Sports	Caguas	...	NaN	1073	48	NaN	http://images.acmesports.sports/Pelican+Sunstr...	Pelican Sunstream 100 Kayak	199.990005	12/21/2016 8:06	Second Class
77071	DEBIT	4	2	33.790001	99.959999	Late delivery	1	46	Indoor/Outdoor Games	Caguas	...	NaN	1014	46	NaN	http://images.acmesports.sports/O%27Brien+Men%...	O'Brien Men's Neoprene Life Vest	49.980000	10/5/2017 4:54	Second Class
79526	TRANSFER	5	2	51.040001	175.990005	Late delivery	1	48	Water Sports	Caguas	...	88220.0	1073	48	NaN	http://images.acmesports.sports/Pelican+Sunstr...	Pelican Sunstream 100 Kayak	199.990005	5/16/2016 8:45	Second Class
101390	DEBIT	4	2	-67.169998	41.980000	Late delivery	1	46	Indoor/Outdoor Games	Caguas	...	98105.0	1014	46	NaN	http://images.acmesports.sports/O%27Brien+Men%...	O'Brien Men's Neoprene Life Vest	49.980000	5/2/2016 8:36	Second Class
111632	CASH	4	2	-10.060000	141.690002	Late delivery	1	46	Indoor/Outdoor Games	Caguas	...	NaN	1014	46	NaN	http://images.acmesports.sports/O%27Brien+Men%...	O'Brien Men's Neoprene Life Vest	49.980000	12/21/2016 8:06	Second Class
131624	DEBIT	3	2	7.440000	185.929993	Late delivery	1	46	Indoor/Outdoor Games	Caguas	...	NaN	1014	46	NaN	http://images.acmesports.sports/O%27Brien+Men%...	O'Brien Men's Neoprene Life Vest	49.980000	2/27/2016 9:44	Second Class
140353	CASH	4	2	16.540001	35.189999	Late delivery	1	29	Shop By Sport	Caguas	...	NaN	627	29	NaN	http://images.acmesports.sports/Under+Armour+G...	Under Armour Girls' Toddler Spine Surge Runni	39.990002	12/21/2016 8:06	Second Class
140355	CASH	4	2	131.119995	278.980011	Late delivery	1	43	Camping & Hiking	Caguas	...	NaN	957	43	NaN	http://images.acmesports.sports/Diamondback+Wo...	Diamondback Women's Serene Classic Comfort Bi	299.980011	12/21/2016 8:06	Second Class
140519	CASH	4	2	46.070000	230.360001	Late delivery	1	17	Cleats	Caguas	...	NaN	365	17	NaN	http://images.acmesports.sports/Perfect+Fitnes...	Perfect Fitness Perfect Rip Deck	59.990002	12/21/2016 8:06	Second Class
141590	DEBIT	2	1	83.989998	299.970001	Late delivery	1	9	Cardio Equipment	Caguas	...	NaN	191	9	NaN	http://images.acmesports.sports/Nike+Men%27s+F...	Nike Men's Free 5.0+ Running Shoe	99.989998	10/11/2016 10:07	First Class
142113	DEBIT	3	2	49.720001	110.489998	Late delivery	1	18	Men's Footwear	Caguas	...	NaN	403	18	NaN	http://images.acmesports.sports/Nike+Men%27s+C...	Nike Men's CJ Elite 2 TD Football Cleat	129.990005	2/27/2016 9:44	Second Class
142133	DEBIT	3	2	18.360001	40.790001	Late delivery	1	37	Electronics	Caguas	...	NaN	818	37	NaN	http://images.acmesports.sports/Titleist+Pro+V...	Titleist Pro V1x Golf Balls	47.990002	2/27/2016 9:44	Second Class
142188	DEBIT	2	2	47.840000	103.989998	Shipping on time	0	18	Men's Footwear	Caguas	...	93727.0	403	18	NaN	http://images.acmesports.sports/Nike+Men%27s+C...	Nike Men's CJ Elite 2 TD Football Cleat	129.990005	6/19/2016 4:20	Second Class
142197	DEBIT	4	2	14.250000	47.500000	Late delivery	1	24	Women's Apparel	Caguas	...	98105.0	502	24	NaN	http://images.acmesports.sports/Nike+Men%27s+D...	Nike Men's Dri-FIT Victory Golf Polo	50.000000	5/2/2016 8:36	Second Class
145528	PAYMENT	6	4	123.410004	339.980011	Late delivery	1	45	Fishing	Caguas	...	NaN	1004	45	NaN	http://images.acmesports.sports/Field+%26+Stre...	Field & Stream Sportsman 16 Gun Fire Safe	399.980011	1/19/2017 3:41	Standard Class
147189	DEBIT	2	1	104.989998	299.989990	Late delivery	1	45	Fishing	Caguas	...	NaN	1004	45	NaN	http://images.acmesports.sports/Field+%26+Stre...	Field & Stream Sportsman 16 Gun Fire Safe	399.980011	10/11/2016 10:07	First Class
148067	DEBIT	4	2	6.640000	58.779999	Late delivery	1	11	Fitness Accessories	Caguas	...	98105.0	235	11	NaN	http://images.acmesports.sports/Under+Armour+H...	Under Armour Hustle Storm Medium Duffle Bag	34.990002	5/2/2016 8:36	Second Class
148163	DEBIT	2	2	34.919998	145.500000	Shipping on time	0	24	Women's Apparel	Caguas	...	93727.0	502	24	NaN	http://images.acmesports.sports/Nike+Men%27s+D...	Nike Men's Dri-FIT Victory Golf Polo	50.000000	6/19/2016 4:20	Second Class
148231	DEBIT	3	2	59.570000	121.559998	Late delivery	1	37	Electronics	Caguas	...	NaN	835	37	NaN	http://images.acmesports.sports/Bridgestone+e6...	Bridgestone e6 Straight Distance NFL Carolina	31.990000	2/27/2016 9:44	Second Class
148317	DEBIT	4	2	36.669998	224.960007	Late delivery	1	17	Cleats	Caguas	...	98105.0	365	17	NaN	http://images.acmesports.sports/Perfect+Fitnes...	Perfect Fitness Perfect Rip Deck	59.990002	5/2/2016 8:36	Second Class
149044	DEBIT	3	4	20.160000	57.590000	Advance shipping	0	17	Cleats	Caguas	...	NaN	365	17	NaN	http://images.acmesports.sports/Perfect+Fitnes...	Perfect Fitness Perfect Rip Deck	59.990002	1/28/2016 22:03	Standard Class
149095	DEBIT	3	4	-84.750000	110.489998	Advance shipping	0	18	Men's Footwear	Caguas	...	NaN	403	18	NaN	http://images.acmesports.sports/Nike+Men%27s+C...	Nike Men's CJ Elite 2 TD Football Cleat	129.990005	1/28/2016 22:03	Standard Class
149161	DEBIT	3	4	18.139999	287.980011	Advance shipping	0	43	Camping & Hiking	Caguas	...	NaN	957	43	NaN	http://images.acmesports.sports/Diamondback+Wo...	Diamondback Women's Serene Classic Comfort Bi	299.980011	1/28/2016 22:03	Standard Class
149171	DEBIT	3	4	103.449997	284.980011	Advance shipping	0	43	Camping & Hiking	Caguas	...	NaN	957	43	NaN	http://images.acmesports.sports/Diamondback+Wo...	Diamondback Women's Serene Classic Comfort Bi	299.980011	1/28/2016 22:03	Standard Class
149426	DEBIT	5	4	107.989998	239.979996	Late delivery	1	43	Camping & Hiking	Caguas	...	91104.0	957	43	NaN	http://images.acmesports.sports/Diamondback+Wo...	Diamondback Women's Serene Classic Comfort Bi	299.980011	6/27/2016 6:15	Standard Class
150152	DEBIT	3	4	-160.059998	218.360001	Advance shipping	0	17	Cleats	Caguas	...	NaN	365	17	NaN	http://images.acmesports.sports/Perfect+Fitnes...	Perfect Fitness Perfect Rip Deck	59.990002	1/28/2016 22:03	Standard Class
150631	PAYMENT	2	1	15.130000	58.189999	Late delivery	1	17	Cleats	Caguas	...	43615.0	365	17	NaN	http://images.acmesports.sports/Perfect+Fitnes...	Perfect Fitness Perfect Rip Deck	59.990002	4/19/2016 9:29	First Class
150638	PAYMENT	2	1	50.330002	114.389999	Late delivery	1	18	Men's Footwear	Caguas	...	43615.0	403	18	NaN	http://images.acmesports.sports/Nike+Men%27s+C...	Nike Men's CJ Elite 2 TD Football Cleat	129.990005	4/19/2016 9:29	First Class
150650	PAYMENT	2	1	-390.570007	251.979996	Late delivery	1	43	Camping & Hiking	Caguas	...	43615.0	957	43	NaN	http://images.acmesports.sports/Diamondback+Wo...	Diamondback Women's Serene Classic Comfort Bi	299.980011	4/19/2016 9:29	First Class
150761	PAYMENT	2	1	3.920000	196.000000	Late delivery	1	24	Women's Apparel	Caguas	...	43615.0	502	24	NaN	http://images.acmesports.sports/Nike+Men%27s+D...	Nike Men's Dri-FIT Victory Golf Polo	50.000000	4/19/2016 9:29	First Class
153030	PAYMENT	2	1	73.080002	347.980011	Late delivery	1	45	Fishing	Caguas	...	43615.0	1004	45	NaN	http://images.acmesports.sports/Field+%26+Stre...	Field & Stream Sportsman 16 Gun Fire Safe	399.980011	4/19/2016 9:29	First Class

	Type	Days for shipping (real)	Days for shipment (scheduled)	Benefit per order	Sales per customer	Delivery Status	Late_delivery_risk	Category Id	Category Name	Customer City	...	Order Zipcode	Product Card Id	Product Category Id	Product Description	Product Image	Product Name	Product Price	Product Status	shipping date (DateOrders)	Shipping Mode
35704	DEBIT	2	1	66.379997	189.660004	Late delivery	1	65	Consumer Electronics	CA	...	NaN	1352	65	NaN	http://images.acmesports.sports/Industrial+con...	Industrial consumer electronics	252.880005	0	11/4/2017 18:31	First Class
46440	PAYMENT	4	4	10.910000	38.959999	Shipping on time	0	75	Video Games	CA	...	NaN	1362	75	NaN	http://images.acmesports.sports/Fighting+video...	Fighting video games	39.750000	0	12/14/2017 15:18	Standard Class

	CCity_1	CCity_2	CCity_3	CCity_4	CCity_5	CCity_6	CCity_7	CCity_8	CState_1	CState_2	...	Customer Country_USA	Market_Europe	Market_LATAM	Market_Pacific Asia	Market_USCA	Shipping Mode_Same Day	Shipping Mode_Second Class	Shipping Mode_Standard Class	Customer Segment	Sales
0	1	0	0	0	0	0	0	0	0	0	...	0	0	0	1	0	0	0	1	1	327.750000
1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	1	0	0	0	1	1	327.750000
2	0	0	0	0	1	0	0	0	0	1	...	1	0	0	1	0	0	0	1	1	327.750000
3	0	0	0	0	0	1	0	0	0	1	...	1	0	0	1	0	0	0	1	3	327.750000
4	1	0	0	0	0	0	0	0	0	0	...	0	0	0	1	0	0	0	1	2	327.750000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
180514	0	0	0	1	0	0	0	0	1	0	...	1	0	0	1	0	0	0	1	3	399.980011
180515	0	0	0	0	0	0	1	0	0	1	...	1	0	0	1	0	0	1	0	2	399.980011
180516	1	0	0	0	0	0	0	0	0	0	...	1	0	0	1	0	0	0	1	2	399.980011
180517	1	0	0	0	0	0	0	0	0	0	...	0	0	0	1	0	0	0	1	1	399.980011
180518	1	0	0	0	0	0	0	0	0	0	...	0	0	0	1	0	0	0	1	1	399.980011

	index	Coefficient
0	Order Item Discount	0.999985
1	Sales per customer	0.500000
2	Order Item Total	0.500000
3	shipping Year	0.000245
4	OCountry_3	0.000090
...	...	...
96	ORegion_5	-0.000057
97	Customer Country_USA	-0.000062
98	MonthOrders	-0.000354
99	Order Item Discount Rate	-0.001929
100	YearOrders	-0.004345

	index	Coefficient
0	Order Item Discount	0.999984
1	Order Item Total	0.500001
2	Sales per customer	0.500000
3	OCountry_3	0.000088
4	Market_USCA	0.000074
...	...	...
96	Customer Country_USA	-0.000062
97	MonthOrders	-0.000065
98	shipping Year	-0.000719
99	YearOrders	-0.000871
100	Order Item Discount Rate	-0.001903

	index	Coefficient
0	Sales per customer	1.050423
1	Order Item Discount	0.999711
2	Benefit per order	0.153694
3	Product Price	0.039230
4	Product Card Id	0.004288
...	...	...
96	Order Item Id	-0.000017
97	Order Item Cardprod Id	-0.004311
98	Order Item Product Price	-0.039212
99	Order Item Total	-0.050404
100	Order Profit Per Order	-0.153687

	CCity_1	CCity_2	CCity_3	CCity_4	CCity_5	CCity_6	CCity_7	CCity_8	CState_1	CState_2	...	Delivery Status_Shipping on time	Customer Country_USA	Market_Europe	Market_LATAM	Market_Pacific Asia	Market_USCA	Shipping Mode_Same Day	Shipping Mode_Second Class	Shipping Mode_Standard Class	Customer Segment
57540	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	1	0	0	0	1	1
33860	0	0	0	0	0	1	0	0	0	0	...	0	1	0	0	1	0	0	0	1	1
103781	1	0	0	0	0	0	0	0	0	0	...	0	0	1	0	0	0	0	1	0	2
141638	1	0	0	0	0	0	0	0	0	0	...	0	0	0	1	0	0	0	0	0	3
68069	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	1	0	0	1	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
38178	0	0	0	1	0	0	0	0	0	1	...	0	1	0	0	1	0	0	1	0	1
25905	1	0	0	0	0	0	0	0	0	0	...	0	0	1	0	0	0	0	0	1	2
13855	0	0	0	0	0	0	0	1	0	1	...	1	1	1	0	0	0	0	0	1	2
154978	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	1	0	0	0	1	1
25774	0	1	0	0	0	0	0	0	0	1	...	0	1	1	0	0	0	0	0	0	2

	MLA used	Train Accuracy	Test Accuracy	MAE	MSE	RMSE	R² Score	Adjusted R² Score
0	LinearRegression	1.0000	1.0000	0.000575	0.000002	0.001493	1.000000	1.000000
1	Ridge	1.0000	1.0000	0.000575	0.000002	0.001493	1.000000	1.000000
2	Lasso	1.0000	1.0000	0.013108	0.000423	0.020577	1.000000	1.000000
5	BayesianRidge	1.0000	1.0000	0.000575	0.000002	0.001493	1.000000	1.000000
3	DecisionTreeRegressor	1.0000	0.9999	0.017987	1.234685	1.111164	0.999928	0.999927
4	RandomForestRegressor	0.8602	0.8566	38.964421	2444.262921	49.439487	0.856608	0.855801

	Label	Prediction
96591	1	1
47035	0	0
56372	0	0
51696	0	1
126717	0	0
...	...	...
94866	0	0
47232	1	1
19757	1	1
168841	1	1
95973	0	0

	Label	Prediction
96591	1	1
47035	0	0
56372	0	0
51696	0	0
126717	0	0
...	...	...
94866	0	0
47232	1	1
19757	1	1
168841	1	1
95973	0	0

	index	Coefficient
0	Sales per customer	1.049876
1	Order Item Discount	0.999710
2	Benefit per order	0.155533
3	Product Price	0.039267
4	Product Card Id	0.004327
...	...	...
96	Order Item Id	-0.000016
97	Order Item Cardprod Id	-0.004351
98	Order Item Product Price	-0.039249
99	Order Item Total	-0.049857
100	Order Profit Per Order	-0.155526

	Value
shipping Day	0.251333
DayOrders	0.219573
Days for shipment (scheduled)	0.168283
HourOrders	0.055392
OStatus_2	0.034928
...	...
Customer Country_USA	0.000043
Shipping Mode_Standard Class	0.000000
ORegion_1	0.000000
ORegion_2	0.000000
OStatus_1	0.000000

	Value
Days for shipment (scheduled)	0.096198
Shipping Mode_Standard Class	0.083443
shipping Day	0.037013
DayOrders	0.035065
shipping Hour	0.034681
...	...
CState_6	0.000582
CState_3	0.000576
ORegion_1	0.000000
ORegion_2	0.000000
OStatus_1	0.000000

	MLA used	Train Accuracy	Test Accuracy	Precission	Recall	AUC	F1-Score
4	XGBClassifier	1.0000	0.9999	0.999899	1.000000	0.999939	0.999949
2	BaggingClassifier	0.9999	0.9920	0.996486	0.988880	0.992326	0.992669
0	DecisionTreeClassifier	1.0000	0.9631	0.965817	0.966793	0.962654	0.966305
1	RandomForestClassifier	1.0000	0.9200	0.956747	0.894415	0.922696	0.924532
3	GradientBoostingClassifier	0.9199	0.9181	0.923670	0.927218	0.917160	0.925440

Context¶

Import Libraries For Overview & EDA¶

Get The Dataset¶

Overview Of The Dataset¶

Data Cleaning¶

Columns¶

Type¶

Days for shipping (real)¶

Days for shipment (scheduled)¶

Benefit per order¶

Sales per customer¶

Delivery Status¶

Late_delivery_risk¶

Category Id¶

Category Name¶

Customer City¶

Customer Country¶

Customer Email¶

Customer Fname¶

Customer Id¶

Customer Lname¶

Customer Password¶

Customer Segment¶

Customer State¶

Customer Street¶

Customer Zipcode¶

Department Id¶

Department Name¶

Latitude¶

Longitude¶

Market¶

Order City¶

Order Country¶

Order Customer Id¶

Order date (DateOrders)¶

Order Id¶

Order Item Cardprod Id¶

Order Item Discount¶

Order Item Discount Rate¶

Order Item Id¶

Order Item Product Price¶

Order Item Profit Ratio¶

Order Item Quantity¶

Sales¶

Order Item Total¶

Order Profit Per Order¶

Order Region¶

Order State¶

Order Status¶

Order Zipcode¶

Product Card Id¶

Product Category Id¶

Product Description¶

Product Image¶

Product Name¶

Product Price¶

Product Status¶

Shipping date (DateOrders)¶

Shipping Mode¶

Drop Non-Important Columns¶¶

Drop Missing Data¶

EDA: Exploratory Data Analysis¶

Auto EDA with Sweetviz¶

Association Rule Mining: Apriori¶

Build Dataset For Predicting¶

Hash Encoding¶

Train Valid Test Split¶

Sale Prediction¶

Linear Regression¶

Evaluation¶

Ridge Regression¶

Evaluation¶

Lasso Regression¶

Evaluation¶

Decision Tree Regression¶

Evaluation¶

Random Forest Regression¶

Evaluation¶

Bayesian Regression¶

Evaluation¶